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fit
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s
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s
if
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E
d
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tio
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ata
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in
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Mo
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Pre
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T
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s
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d
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e
CC B
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-
SA
li
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se
.
C
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s
p
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ing
A
uth
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:
Su
s
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il Sh
r
esth
a
Dep
ar
tm
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f
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p
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Scie
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u
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n
p
1.
I
NT
RO
D
UCT
I
O
N
Mo
o
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le
(
Mo
d
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lar
Ob
ject
-
Or
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ted
Dy
n
am
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L
ea
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E
n
v
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MS)
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twar
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th
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allo
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in
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tr
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s
to
p
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s
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ar
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co
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r
s
e
d
o
cu
m
en
ts
,
ass
ig
n
m
en
ts
,
q
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izze
s
,
v
id
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m
ater
ials
,
etc.
f
o
r
s
tu
d
en
ts
’
en
ab
lin
g
q
u
ality
o
n
l
in
e
lear
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i
n
g
en
v
ir
o
n
m
en
t.
I
t
is
a
Vir
tu
al
L
ea
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n
in
g
E
n
v
ir
o
n
m
en
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(
VL
E
)
o
r
a
v
ir
t
u
al
p
latf
o
r
m
f
o
r
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s
er
s
to
co
m
m
u
n
icate
,
le
ar
n
an
d
p
ar
ticip
ate
[
1
]
.
I
t
is
also
ca
lled
L
ea
r
n
in
g
Ma
n
ag
em
en
t
Sy
s
tem
(
L
MS)
th
at
en
ab
les
co
u
r
s
e
in
s
tr
u
ct
o
r
s
to
k
ee
p
tr
ac
k
o
f
s
tu
d
en
ts
an
d
ass
ess
th
eir
lea
r
n
in
g
p
e
r
f
o
r
m
an
ce
,
ev
alu
ate
th
e
lear
n
in
g
m
ater
ial
s
u
ch
a
s
ass
ig
n
m
en
ts
an
d
q
u
izze
s
,
h
o
s
t
tex
t
-
b
ased
an
d
v
id
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lectu
r
es.
Mo
o
d
le
n
o
t
o
n
ly
o
f
f
er
s
a
p
latf
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r
m
to
h
o
s
t
v
id
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an
d
tex
t
lectu
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es,
ass
ig
n
m
en
t,
an
d
q
u
izze
s
b
u
t
al
s
o
o
f
f
er
co
llab
o
r
ativ
e
lear
n
in
g
with
o
n
lin
e
d
is
cu
s
s
io
n
f
o
r
u
m
an
d
k
ee
p
s
tr
ac
k
o
f
s
tu
d
en
t
ac
tiv
ity
v
ia
its
lo
g
g
i
n
g
s
y
s
tem
[
2
]
.
So
,
th
e
r
e
is
a
g
r
o
win
g
u
s
e
o
f
th
e
Mo
o
d
le
s
y
s
tem
f
o
r
o
n
lin
e
lear
n
in
g
.
Stu
d
en
ts
in
eter
ac
t
with
th
e
m
o
o
d
le
s
y
s
tem
to
ass
e
s
s
th
e
co
u
r
s
e
co
n
ten
ts
an
d
co
u
r
s
e
m
a
ter
ials
.
Du
e
to
th
is
,
s
tu
d
en
ts
p
r
o
d
u
ce
a
h
u
g
e
am
o
u
n
t
o
f
d
ata
th
r
o
u
g
h
th
eir
in
ter
ac
tio
n
with
th
e
s
y
s
tem
f
o
r
ex
am
p
le;
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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6
I
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t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
10
,
No
.
1
,
A
p
r
il
20
2
1
:
9
–
18
10
s
tu
d
en
ts
’
en
r
o
lm
en
t,
atten
d
a
n
ce
r
ec
o
r
d
s
an
d
ex
am
in
atio
n
r
e
s
u
lts
[
3
]
.
T
h
ese
v
ar
ieties
o
f
d
ata
can
b
e
an
aly
ze
d
to
ex
tr
ac
t
m
ea
n
in
g
f
u
l
in
f
o
r
m
a
ti
o
n
.
So
,
it
is
im
p
o
r
tan
t
to
im
p
lem
en
t
th
e
co
n
c
ep
t
o
f
E
d
u
ca
tio
n
al
Data
Min
in
g
(
E
DM
)
to
ex
tr
ac
t
u
s
ef
u
l k
n
o
w
led
g
e
f
r
o
m
r
aw
d
ata
.
E
DM
is
t
h
e
p
r
o
ce
s
s
o
f
co
n
v
er
tin
g
r
aw
d
ata
co
llected
f
r
o
m
th
e
ed
u
ca
tio
n
al
s
y
s
tem
in
to
m
ea
n
in
g
f
u
l
i
n
f
o
r
m
atio
n
[
4
]
.
He
n
ce
d
ata
m
in
in
g
in
ed
u
ca
tio
n
also
ca
lled
E
DM
is
o
n
e
o
f
th
e
em
er
g
i
n
g
r
esear
ch
ar
ea
s
.
I
t
is
u
s
ef
u
l
in
m
a
n
y
d
if
f
er
en
t
ap
p
licatio
n
ar
ea
s
as
all
t
h
e
p
r
o
b
lem
s
r
elate
d
to
th
e
ed
u
ca
tio
n
al
en
v
ir
o
n
m
e
n
t
ca
n
b
e
m
an
ag
e
d
o
r
r
eso
l
v
ed
u
s
in
g
E
DM
s
u
ch
as
id
en
tifyin
g
w
ea
k/st
r
o
n
g
lea
n
ers
,
id
en
tifyin
g
lea
r
n
in
g
n
ee
d
s
,
r
ed
u
cin
g
d
r
o
p
o
u
t
r
a
tes
,
en
h
a
n
cin
g
a
ca
d
emic
a
ch
ieve
men
t
b
y
imp
r
o
vin
g
lea
r
n
ers
’
p
erfo
r
ma
n
ce
,
imp
r
o
vin
g
tea
c
h
in
g
/lea
r
n
in
g
p
r
o
ce
s
s
es
an
d
s
o
o
n
.
So
,
t
h
e
a
n
aly
s
is
o
f
u
s
er
'
s
o
n
lin
e
(
OL
)
ac
tiv
ities
ca
n
b
e
v
er
y
u
s
ef
u
l
to
id
en
tif
y
t
h
e
h
i
d
d
en
p
at
ter
n
s
an
d
e
x
tr
ac
t
m
ea
n
in
g
f
u
l
i
n
f
o
r
m
atio
n
to
b
u
ilt
th
e
m
o
d
el
u
s
ef
u
l
f
o
r
p
r
ed
icti
o
n
.
R
ap
id
g
r
o
wth
o
f
e
d
u
ca
tio
n
al
d
ata
clea
r
s
to
th
e
f
ac
t
th
a
t
d
is
till
in
g
o
f
h
u
g
e
am
o
u
n
t
o
f
d
ata
r
e
q
u
ir
es
ap
p
r
o
p
r
iate
d
ata
m
in
i
n
g
alg
o
r
ith
m
f
o
r
th
e
a
p
p
r
o
p
r
iate
s
o
l
u
tio
n
[
5
].
Data
Min
in
g
is
th
e
ex
tr
ac
tio
n
o
f
k
n
o
wled
g
e
f
r
o
m
th
e
lar
g
e
s
to
r
ag
e
o
f
d
ata
.
Hen
ce
i
t
is
im
p
o
r
tan
t
to
u
s
e
E
DM
in
o
r
d
e
r
to
u
tili
ze
th
e
r
ep
o
r
ts
th
at
m
o
o
d
le
k
ee
p
s
as
well
as
to
an
aly
ze
an
d
to
b
u
ild
p
r
e
d
ictiv
e
m
o
d
els ab
o
u
t
th
e
ac
tiv
ity
o
f
th
e
s
tu
d
en
ts
th
at
ar
e
u
s
in
g
th
e
s
y
s
tem
[
4
]
.
E
DM
u
s
es c
o
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es to
an
aly
ze
ed
u
ca
tio
n
al
d
ata
in
o
r
d
er
to
s
tu
d
y
q
u
esti
o
n
s
[
6
]
.
I
t
is
co
n
ce
r
n
ed
with
d
e
v
elo
p
in
g
m
eth
o
d
s
th
at
d
is
co
v
e
r
u
s
ef
u
l
k
n
o
wled
g
e
f
r
o
m
d
ata
o
r
ig
in
atin
g
f
r
o
m
ed
u
ca
t
io
n
al
en
v
ir
o
n
m
en
ts
.
I
t
u
t
ilizes
DM
m
eth
o
d
s
to
b
etter
u
n
d
er
s
tan
d
s
tu
d
en
t’
s
p
er
f
o
r
m
an
ce
in
an
ed
u
ca
tio
n
al
s
y
s
tem
[
7
,
8
]
.
T
h
er
e
ar
e
s
ev
er
al
p
o
p
u
lar
m
eth
o
d
s
o
f
E
DM
wh
ich
ca
n
b
e
ap
p
lied
in
ed
u
ca
tio
n
al
d
ata
s
u
ch
as
cla
s
s
if
ica
tio
n
,
clu
s
teri
n
g
an
d
r
eg
r
ess
io
n
.
C
las
s
if
icati
o
n
is
a
p
r
o
ce
d
u
r
e
in
wh
ich
in
d
i
v
id
u
al
item
s
ar
e
p
lace
d
in
to
g
r
o
u
p
s
b
ased
o
n
q
u
an
titativ
e
in
f
o
r
m
atio
n
r
eg
a
r
d
in
g
o
n
e
o
r
m
o
r
e
ch
ar
ac
ter
is
tics
in
h
er
en
t
in
th
e
item
s
an
d
b
ased
o
n
a
tr
ai
n
in
g
s
et
o
f
p
r
e
v
io
u
s
ly
lab
eled
item
s
[
6
,
9
]
.
Pre
d
ictio
n
o
f
s
tu
d
en
t
p
er
f
o
r
m
an
ce
,
p
r
ed
i
ctio
n
o
f
d
r
o
p
o
u
t
an
d
r
eten
tio
n
is
th
e
m
o
s
t
p
o
p
u
lar
ap
p
licati
o
n
o
f
class
if
icatio
n
alg
o
r
ith
m
in
E
DM
.
K
-
Nea
r
est
n
eig
h
b
o
r
s
,
Dec
is
io
n
T
r
ee
,
Na
ïv
e
B
ay
es,
R
an
d
o
m
Fo
r
est,
et
c.
ar
e
th
e
m
o
s
t
u
s
ed
class
if
icatio
n
alg
o
r
ith
m
.
C
lu
s
ter
in
g
is
a
tech
n
iq
u
e
o
f
g
r
o
u
p
in
g
t
h
e
s
tu
d
en
ts
ac
co
r
d
in
g
t
o
th
eir
lear
n
i
n
g
a
n
d
in
ter
ac
tio
n
p
atter
n
s
[
1
0
]
.
R
ec
o
m
m
en
d
atio
n
o
f
r
eso
u
r
ce
s
,
u
n
d
er
s
tan
d
i
n
g
,
a
n
d
p
r
ev
en
tin
g
ac
ad
em
ic
f
ail
u
r
e
(
ex
am
f
ailu
r
e
)
am
o
n
g
u
n
i
v
er
s
ity
s
tu
d
en
ts
ar
e
th
e
co
m
m
o
n
ap
p
licatio
n
o
f
clu
s
ter
in
g
in
E
DM
.
Hier
ar
ch
ical
clu
s
te
r
in
g
an
d
K
-
m
ea
n
s
ar
e
m
o
s
tly
u
s
ed
clu
s
ter
in
g
alg
o
r
ith
m
.
R
eg
r
ess
io
n
is
a
d
ata
m
in
in
g
tech
n
iq
u
e
u
s
ed
to
p
r
ed
ict
a
r
a
n
g
e
o
f
n
u
m
er
ic
v
alu
es
(
also
ca
lled
co
n
tin
u
o
u
s
v
alu
es),
g
iv
e
n
a
p
ar
ticu
la
r
d
ataset.
I
n
E
DM
,
r
eg
r
ess
io
n
an
aly
s
i
s
h
as
b
ee
n
u
s
ed
to
p
r
e
d
ict
a
s
tu
d
e
n
t’
s
k
n
o
wled
g
e.
R
eg
r
ess
io
n
h
as
also
b
ee
n
a
p
p
lied
f
o
r
p
r
ed
ictin
g
wh
eth
e
r
th
e
s
tu
d
e
n
t
will
an
s
wer
a
q
u
esti
o
n
co
r
r
ec
tly
en
o
u
g
h
,
a
n
d
also
to
c
r
ea
te
a
m
o
d
el
th
at
illu
s
tr
ates
th
e
u
s
er
’
s
lear
n
i
n
g
b
eh
av
io
r
[
6
]
.
So
,
i
n
co
n
clu
s
io
n
,
E
DM
ca
n
b
e
d
escr
ib
ed
as
th
e
p
r
o
ce
s
s
,
wh
ich
d
ea
ls
with
th
e
au
to
m
atic
ex
t
r
ac
tio
n
an
d
an
aly
s
is
o
f
d
ata
f
r
o
m
lar
g
e
s
ets
o
f
d
ata
to
ex
p
lo
r
e
p
r
ev
io
u
s
ly
u
n
k
n
o
wn
p
atter
n
s
[
1
1
]
.
T
h
is
r
esear
ch
aim
e
d
t
o
ap
p
ly
E
DM
to
o
ls
an
d
tech
n
iq
u
es
to
an
aly
ze
s
tu
d
e
n
ts
’
OL
d
ata
to
d
ev
elo
p
a
p
r
ed
ictio
n
m
o
d
el
o
f
s
tu
d
e
n
t
p
er
f
o
r
m
an
ce
.
On
e
o
f
th
e
E
DM
m
eth
o
d
s
i.e
.
,
cla
s
s
ifica
tio
n
is
m
ain
ly
u
s
ed
f
o
r
s
tu
d
en
ts
’
p
er
f
o
r
m
an
ce
an
aly
s
i
s
an
d
p
r
ed
ictio
n
.
T
h
e
class
if
icatio
n
tech
n
iq
u
e
is
a
s
u
p
er
v
is
e
d
lear
n
in
g
alg
o
r
ith
m
th
at
ca
n
b
e
u
s
ed
t
o
p
r
ed
ict
ca
teg
o
r
ical
class
lab
els
[
1
2
]
.
T
o
b
u
il
d
a
p
r
ed
ictiv
e
m
o
d
el,
d
if
f
er
en
t
class
if
icatio
n
tech
n
iq
u
es
s
u
ch
as
K
-
N
ea
r
est
N
eig
h
b
o
u
r
(
K
N
N
)
,
N
a
ïve
B
a
ye
s
(
N
B
)
,
S
u
p
p
o
r
t
V
ec
to
r
Ma
ch
in
e
(
S
V
M),
C
A
R
T
d
ec
is
io
n
tr
ee
,
an
d
R
a
n
d
o
m
F
o
r
est
(
R
F
)
ar
e
ap
p
lied
in
t
h
is
r
esear
ch
.
Fu
r
th
er
,
it
also
in
cl
u
d
es
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
th
ese
m
o
d
els
to
f
i
n
d
th
e
b
est
class
if
ier
wh
ich
h
elp
s
in
th
e
ea
r
ly
id
en
tific
atio
n
o
f
s
tr
o
n
g
an
d
wea
k
s
tu
d
en
ts
in
th
e
co
u
r
s
e.
Als
o
,
th
is
r
esear
ch
aim
s
to
f
in
d
th
e
p
ar
am
eter
s
af
f
ec
tin
g
t
h
e
p
er
f
o
r
m
an
ce
o
f
s
tu
d
en
ts
.
2.
RE
L
AT
E
D
WO
RK
S
In
[
1
3
]
,
th
e
au
th
o
r
s
h
ad
ap
p
lied
th
r
ee
wid
ely
u
s
ed
d
ec
is
io
n
tr
ee
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
I
D3
,
C
4
.
5
,
an
d
C
AR
T
f
o
r
th
e
class
if
icatio
n
task
.
T
h
e
m
ain
g
o
al
o
f
th
is
r
esear
ch
was
to
p
r
ed
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
i
n
th
e
f
in
al
e
x
a
m
.
Fo
r
t
h
is
s
tu
d
y
,
d
ata
f
r
o
m
9
0
en
g
in
ee
r
in
g
s
tu
d
e
n
ts
wer
e
co
llected
f
r
o
m
t
h
e
I
n
s
titu
te
o
f
E
n
g
in
ee
r
i
n
g
a
n
d
T
ec
h
n
o
lo
g
y
at
VB
S
Pu
r
v
a
n
ch
al
Un
iv
er
s
ity
,
J
au
n
p
u
r
(
Uttar
Pr
ad
esh
)
.
T
h
is
s
tu
d
y
im
p
lem
en
ted
t
h
e
c
o
m
p
ar
ativ
e
an
aly
s
is
am
o
n
g
I
D3
,
C
4
.
5
,
a
n
d
C
AR
T
in
th
e
W
ek
a
to
o
l.
T
h
e
ev
al
u
atio
n
wa
s
d
o
n
e
u
s
in
g
1
0
-
f
o
l
d
cr
o
s
s
-
v
alid
atio
n
b
ased
o
n
ac
cu
r
ac
y
an
d
tim
e
o
f
ex
ec
u
tio
n
wh
er
e
it
was
f
o
u
n
d
th
at
C
4
.
5
h
ad
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
6
7
.
7
7
7
8
%
th
an
o
th
e
r
class
if
ier
s
s
u
ch
as
I
D3
an
d
C
AR
T
with
th
e
s
am
e
ac
cu
r
ac
y
o
f
6
2
.
2
2
2
2
%.
Ho
wev
er
,
th
e
e
x
ec
u
tio
n
tim
e
to
b
u
ild
th
e
m
o
d
el
was
less
f
o
r
I
D3
with
0
.
0
0
s
ec
th
a
n
f
o
r
C
4
.
5
with
0
.
0
3
s
ec
an
d
C
AR
T
with
0
.
0
9
s
ec
.
T
h
e
r
an
d
o
m
f
o
r
est
m
eth
o
d
w
as
u
s
ed
in
an
o
th
er
r
esear
ch
t
o
ex
am
in
e
th
e
im
p
o
r
tan
t
v
ar
i
ab
les
[
1
4
]
.
T
h
e
d
ata
wer
e
co
llected
f
r
o
m
th
e
o
n
lin
e
d
is
cu
s
s
io
n
-
b
ase
d
lear
n
in
g
as
th
e
f
ir
s
t
b
len
d
e
d
lear
n
in
g
class
an
d
lectu
r
e
-
b
ased
lea
r
n
in
g
as
th
e
s
ec
o
n
d
b
len
d
e
d
lea
r
n
in
g
class
.
T
h
is
s
tu
d
y
c
o
m
p
a
r
ed
th
e
p
r
ed
ictio
n
m
o
d
el
b
ased
o
n
th
ese
c
o
llected
d
ata.
T
h
e
e
x
p
er
im
en
ted
r
esu
lt
r
ev
ea
led
th
at
in
d
is
cu
s
s
io
n
-
b
ased
lear
n
i
n
g
,
ac
tiv
e
lear
n
e
r
’
s
p
ar
ticip
atio
n
in
o
n
lin
e
f
o
r
u
m
a
f
f
ec
t stu
d
en
t’
s
ac
h
iev
em
e
n
t w
h
ile
in
lectu
r
e
-
b
ased
lear
n
in
g
,
s
u
b
m
itti
n
g
task
s
o
r
d
o
wn
lo
ad
in
g
m
ater
ial
as
m
ai
n
o
n
lin
e
ac
tiv
ities
d
o
esn
’
t
af
f
ec
t
s
tu
d
en
t’
s
ac
h
ie
v
em
en
t
b
u
t
o
n
ly
lo
g
f
r
e
q
u
en
c
y
d
o
es
af
f
ec
t
i.e
.
b
o
th
ca
s
es
i
n
d
icate
d
d
if
f
er
e
n
t
im
p
o
r
tan
t
f
ea
tu
r
es
f
o
r
th
e
p
r
ed
ictio
n
m
o
d
el.
Similar
ly
,
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
d
u
ca
tio
n
a
l d
a
t
a
min
in
g
i
n
m
o
o
d
le
d
a
ta
(
S
u
s
h
il S
h
r
esth
a
)
11
an
o
th
er
s
tu
d
y
,
class
if
icatio
n
tech
n
iq
u
es
s
u
c
h
as
Dec
is
io
n
T
r
ee
,
Naïv
e
B
ay
es,
an
d
R
u
le
-
B
ased
wer
e
ap
p
lied
f
o
r
th
e
d
ata
m
in
in
g
p
r
o
ce
s
s
[
1
5
]
.
T
h
e
m
ain
g
o
al
o
f
th
is
s
tu
d
y
was
to
p
r
ed
ict
s
tu
d
en
ts
’
ac
a
d
em
ic
p
er
f
o
r
m
an
ce
u
s
in
g
th
e
class
if
icatio
n
tech
n
i
q
u
e.
Data
f
r
o
m
4
9
7
s
tu
d
e
n
ts
wer
e
co
llected
th
at
in
clu
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ed
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h
eir
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em
o
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ap
h
ics,
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r
ev
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s
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ad
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ic
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ec
o
r
d
s
,
a
n
d
f
am
ily
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ac
k
g
r
o
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d
in
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o
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m
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.
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o
m
t
h
e
e
x
p
er
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e
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ted
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esu
lt,
th
e
s
tu
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y
s
h
o
wed
R
u
le
-
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ased
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e
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e
s
t
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o
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el
with
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ig
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est
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es
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ch
as Na
ïv
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ay
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r
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d
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r
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[
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5
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n
[
1
6
]
,
f
o
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r
class
if
icatio
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tech
n
iq
u
es
s
u
ch
as
R
an
d
o
m
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r
est,
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ay
es,
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-
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r
est
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h
b
o
u
r
,
an
d
Dec
is
io
n
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r
ee
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e
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lied
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o
r
th
e
s
tu
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p
er
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r
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ce
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n
.
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h
is
s
tu
d
y
also
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n
d
u
cted
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co
m
p
ar
ativ
e
a
n
aly
s
is
o
f
th
e
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o
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r
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ier
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o
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els
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ased
o
n
Acc
u
r
ac
y
,
Pre
cisi
o
n
,
R
ec
all,
a
n
d
F
-
Me
asu
r
e.
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r
th
is
s
tu
d
y
,
d
ata
f
r
o
m
2
6
s
tu
d
en
ts
wer
e
co
llec
ted
with
a
to
tal
o
f
1
1
f
ea
tu
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es
s
u
ch
as
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o
u
r
s
eV
iew
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s
s
g
in
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iew
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s
s
ig
n
_
s
u
b
mit_
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p
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te,
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eso
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r
ce
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iew
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o
r
u
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iew
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T
I
(
Ove
r
a
ll
s
co
r
e
in
p
r
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g
r
a
mmin
g
tech
n
iq
u
e
I
)
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T
I
I
(
Ove
r
a
ll
s
co
r
e
in
p
r
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g
r
a
mmin
g
tech
n
i
q
u
e
I
I
)
,
A
s
s
ig
n
men
t
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to
ta
l
s
co
r
e
in
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ll
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s
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ig
n
me
n
t)
,
L
a
b
To
ta
l
(
t
o
ta
l
s
co
r
e
in
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ll
la
b
w
o
r
k)
,
Mid
te
r
m
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d
P
erfo
r
ma
n
ce
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tu
d
en
t
s
’
o
ve
r
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ll
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r
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e
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s
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w
,
med
iu
m
a
n
d
h
ig
h
)
.
T
h
e
ex
p
er
im
en
ted
r
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lt
r
ev
ea
led
th
at
R
an
d
o
m
Fo
r
est
h
ad
a
b
et
ter
p
er
f
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r
m
an
ce
with
an
ac
cu
r
ac
y
o
f
7
6
.
9
%
th
an
KNN
(
6
9
.
2
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d
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is
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n
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r
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(
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1
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5
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b
u
t
it
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t
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er
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m
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y
Naïv
e
B
ay
es
with
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ac
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r
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y
o
f
9
3
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%.
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M
E
T
H
O
DS
T
h
is
s
ec
tio
n
d
is
cu
s
s
es th
e
r
ese
ar
ch
m
eth
o
d
s
f
o
llo
wed
i
n
th
is
r
esear
ch
.
3
.
1
.
D
a
t
a
c
o
llect
io
n
T
h
is
s
tu
d
y
c
o
llected
th
e
d
at
a
f
r
o
m
s
tu
d
e
n
ts
en
r
o
lle
d
in
th
e
co
u
r
s
e
C
OM
P
3
4
1
ca
lled
Hu
ma
n
C
o
mp
u
ter
I
n
tera
ctio
n
(
HC
I
)
f
r
o
m
th
e
M
o
o
d
le
s
y
s
tem
o
f
Kath
m
an
d
u
Un
iv
er
s
ity
,
Nep
al
.
T
h
e
ty
p
es
o
f
d
ata
u
s
ed
f
o
r
t
h
e
an
aly
s
is
wer
e
f
r
o
m
s
tu
d
en
ts
’
OL
ac
tiv
ities
s
u
ch
as
S
ys
tem
Lo
g
an
d
Qu
iz
Gra
d
es
.
3
.
1
.
1
.
Sy
s
t
em
lo
g
T
h
e
s
y
s
tem
lo
g
co
n
s
is
ts
o
f
d
a
ta
o
f
ea
c
h
click
m
ad
e
in
th
e
s
y
s
tem
b
y
a
u
s
er
.
T
h
is
d
ataset
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n
s
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ts
o
f
1
4
8
3
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o
b
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er
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s
an
d
9
attr
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tes
o
f
1
2
8
ac
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u
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er
s
in
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e
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y
s
tem
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F
ig
u
r
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1
s
h
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e
s
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le
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y
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ataset.
Fig
u
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Q
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r
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h
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aliza
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eh
in
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th
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ize
t
h
e
p
atter
n
s
o
f
th
e
s
tu
d
en
ts
[
1
7
]
.
So
,
i
n
th
is
r
esear
ch
,
d
if
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er
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n
t
ch
ar
ts
ar
e
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lo
tted
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o
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ata
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elate
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r
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p
le,
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i
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ch
a
r
t
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is
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alize
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er
s
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o
s
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ac
tiv
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ar
ticip
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lin
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o
r
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tio
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t
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alize
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e
r
e
latio
n
s
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ip
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etwe
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.
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etwe
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ep
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n
d
en
t
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es
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u
s
er
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o
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e
ac
tiv
ities
in
th
e
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y
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tem
)
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d
d
ep
en
d
e
n
t
f
ea
tu
r
e
(
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ad
e
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th
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r
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e)
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d
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o
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ta
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lo
t
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is
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alize
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e
m
o
s
t
in
f
lu
en
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g
f
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o
f
th
e
s
tu
d
en
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’
p
er
f
o
r
m
an
ce
in
t
h
e
co
u
r
s
e.
3
.
4
.
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ea
t
ure
s
elec
t
io
n
Feat
u
r
e
s
elec
tio
n
is
th
e
p
r
o
ce
s
s
o
f
s
elec
tin
g
t
h
e
r
ele
v
an
t
f
ea
tu
r
e
s
u
b
s
et
f
r
o
m
th
e
c
o
llectio
n
o
f
wh
o
le
f
ea
tu
r
es
th
at
co
n
tain
ed
s
ev
er
a
l
ir
r
elev
an
t
f
ea
tu
r
es.
So
,
in
th
is
s
tep
,
ir
r
elev
an
t
f
ea
tu
r
es
wer
e
r
em
o
v
e
d
wh
ic
h
co
n
s
eq
u
en
tly
h
el
p
ed
in
b
u
ild
i
n
g
h
ig
h
ac
c
u
r
ac
y
p
r
ed
ictio
n
m
o
d
el.
L
ik
ewise,
th
er
e
ar
e
o
t
h
er
b
en
ef
its
s
u
ch
as
r
ed
u
ce
o
v
er
f
itti
n
g
o
f
d
ata,
r
ed
u
ce
th
e
co
m
p
lex
ity
o
f
co
m
p
u
t
atio
n
,
in
cr
ea
s
e
m
o
d
el
class
if
icatio
n
ac
cu
r
ac
y
,
etc.
[
1
8
]
.
T
h
e
f
ea
tu
r
e
s
elec
tio
n
te
ch
n
iq
u
e
is
v
er
y
u
s
ef
u
l
t
o
an
aly
ze
th
e
r
elatio
n
s
h
ip
b
etw
ee
n
th
e
in
d
ep
en
d
en
t
f
ea
tu
r
es
an
d
d
ep
e
n
d
en
t
f
ea
tu
r
es
i.e
.
,
f
r
o
m
th
e
an
aly
s
is
,
it
h
elp
s
to
id
en
tif
y
th
e
m
o
s
t
in
f
lu
en
cin
g
in
d
ep
en
d
en
t
f
ea
tu
r
es
to
th
e
d
ep
en
d
en
t
f
ea
t
u
r
e
[
1
9
]
.
T
h
e
s
tu
d
y
in
th
is
r
es
ea
r
ch
f
o
c
u
s
es
to
an
aly
ze
th
e
s
tu
d
en
t
OL
f
ea
tu
r
es
th
at
h
av
e
a
g
r
e
ater
im
p
ac
t
o
n
th
e
s
tu
d
en
ts
’
p
e
r
f
o
r
m
an
ce
.
I
n
th
is
s
tu
d
y
,
o
n
e
o
f
th
e
wr
ap
p
er
-
b
ased
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
ca
lled
B
o
r
u
t
a
was
ap
p
lied
f
o
r
th
e
f
ea
tu
r
e
s
elec
tio
n
task
.
B
o
r
u
ta
is
a
wr
ap
p
er
m
eth
o
d
b
u
ilt
ar
o
u
n
d
th
e
r
an
d
o
m
f
o
r
est
class
if
icatio
n
alg
o
r
ith
m
[
2
0
]
.
T
h
e
m
ain
ad
v
an
ta
g
e
o
f
u
s
in
g
B
o
r
u
ta
is
th
at
it
d
ec
id
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
d
u
ca
tio
n
a
l d
a
t
a
min
in
g
i
n
m
o
o
d
le
d
a
ta
(
S
u
s
h
il S
h
r
esth
a
)
13
wh
er
e
a
f
ea
tu
r
e
is
im
p
o
r
tan
t
o
r
n
o
t
i.e
.
it
s
elec
t
th
e
s
tatis
tica
lly
s
ig
n
if
ican
t
f
ea
tu
r
es.
So
,
it
h
elp
s
to
o
b
tain
al
l
th
e
im
p
o
r
tan
t
f
ea
tu
r
es f
r
o
m
th
e
d
ataset
co
n
ce
r
n
in
g
th
e
tar
g
et
f
ea
tu
r
e.
3
.
5
.
Appl
y
in
g
cla
s
s
if
ica
t
io
n
t
ec
hn
iqu
e
C
las
s
if
icatio
n
is
a
s
u
p
er
v
is
ed
lear
n
in
g
tech
n
iq
u
e
u
s
ed
f
o
r
th
e
p
r
ed
ictio
n
o
f
a
p
r
ed
e
f
in
e
d
class
o
r
g
r
o
u
p
.
I
t
is
wid
ely
ap
p
lied
in
s
tu
d
en
ts
’
p
er
f
o
r
m
a
n
ce
p
r
ed
ict
io
n
.
T
h
er
e
ar
e
m
an
y
tech
n
iq
u
es
s
u
ch
as
Dec
is
io
n
T
r
ee
,
Naïv
e
B
ay
es,
SVM
,
Neu
r
al
Netwo
r
k
,
K
Nea
r
est
Neig
h
b
o
u
r
,
a
n
d
R
an
d
o
m
Fo
r
est
th
at
ca
n
b
e
ap
p
lied
to
th
e
s
tu
d
en
ts
’
d
ata
s
et
f
o
r
th
e
class
if
icatio
n
task
[
2
1
]
.
I
n
th
is
s
tu
d
y
,
K
Nea
r
est
Neig
h
b
o
u
r
,
N
aïv
e
B
ay
es,
SVM,
R
an
d
o
m
Fo
r
est,
an
d
C
AR
T
d
ec
is
io
n
tr
ee
wer
e
ap
p
lied
f
o
r
t
h
e
class
if
icatio
n
task
.
3
.
5
.
1
.
K
-
nea
re
s
t
neig
hb
o
ur
K
Nea
r
est
Neig
h
b
o
r
(
KNN)
is
a
class
if
icatio
n
tech
n
iq
u
e
u
s
e
d
f
o
r
class
if
y
in
g
u
n
k
n
o
wn
o
b
j
ec
ts
b
ased
o
n
th
e
clo
s
est
n
eig
h
b
o
r
wh
o
s
e
class
i
s
alr
ea
d
y
k
n
o
wn
(
i.e
.
,
it
is
an
in
s
tan
ce
-
b
ased
class
if
ier
th
at
o
p
er
ates
o
n
un
k
n
o
wn
in
s
tan
ce
s
)
[
2
2
]
.
T
h
is
class
if
ier
r
etain
s
th
e
en
tire
tr
a
in
in
g
s
et
d
u
r
in
g
lear
n
in
g
a
n
d
ass
ig
n
s
a
clas
s
to
a
n
ew
u
n
k
n
o
wn
in
s
tan
ce
r
ep
r
esen
ted
b
y
th
e
m
aj
o
r
v
o
te
o
f
its
n
ea
r
est
n
eig
h
b
o
r
lab
el
in
th
e
t
r
ain
in
g
s
et.
KNN
is
th
e
s
im
p
lest
alg
o
r
ith
m
th
at
is
ea
s
y
to
u
n
d
e
r
s
tan
d
an
d
im
p
le
m
en
t f
o
r
th
e
class
if
icatio
n
task
[
2
3
,
2
4
]
.
3
.
5
.
2
.
Na
ïv
e
ba
y
es
Naïv
e
B
ay
es
(
NB
)
i
s
a
s
im
p
le
p
r
o
b
ab
ilis
tic
class
if
ier
th
at
f
i
n
d
s
a
p
r
o
b
a
b
ilis
tic
r
elatio
n
s
h
ip
b
etwe
en
class
es
an
d
th
eir
attr
ib
u
tes
[
1
9
]
.
NB
alg
o
r
ith
m
is
b
ased
o
n
th
e
B
ay
esian
th
eo
r
em
th
at
co
m
p
u
tes
th
e
p
r
o
b
a
b
ilit
y
o
f
th
e
tar
g
et
o
n
a
g
iv
en
p
r
ed
icto
r
o
r
attr
i
b
u
te
v
a
lu
es.
I
t
is
a
b
etter
p
r
o
b
ab
ilis
tic
class
if
ier
th
at
ca
n
co
m
p
u
te
th
e
m
o
s
t
p
o
s
s
ib
le
o
u
tp
u
t
b
ased
o
n
th
e
in
p
u
t
a
n
d
h
as
p
r
o
v
e
n
to
w
o
r
k
s
atis
f
ac
to
r
ily
in
m
a
n
y
ap
p
licatio
n
d
o
m
ain
s
[
2
1
]
.
I
t is
u
s
ed
wh
en
th
e
d
im
en
s
io
n
ality
o
f
in
p
u
t is h
ig
h
[
1
2
]
.
3
.
5
.
3
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
(
SVM
)
SVM
is
o
n
e
o
f
th
e
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
I
t
is
a
n
ew
class
if
icatio
n
m
eth
o
d
u
s
ed
f
o
r
b
o
th
lin
ea
r
an
d
n
o
n
li
n
ea
r
d
at
a
[
2
3
]
.
I
t
is
o
n
e
o
f
th
e
m
o
s
t
p
o
p
u
lar
class
if
icatio
n
tech
n
iq
u
es to
p
r
ed
ict
ac
cu
r
ate
r
esu
lts
f
o
r
m
o
s
t o
f
t
h
e
class
if
icatio
n
an
d
p
r
ed
ictio
n
p
r
o
b
lem
s
[
1
2
]
.
3
.
5
.
4
.
CART dec
is
io
n t
re
e
C
AR
T
(
C
las
s
if
icatio
n
an
d
R
eg
r
ess
io
n
T
r
ee
s
)
alg
o
r
ith
m
is
a
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
th
at
c
an
b
e
u
s
ed
to
b
u
ild
b
o
th
class
if
icatio
n
a
n
d
r
eg
r
ess
io
n
d
ec
is
io
n
tr
ee
s
.
I
t
ca
n
h
a
n
d
le
b
o
th
th
e
c
ateg
o
r
ical
an
d
n
u
m
er
ical
attr
ib
u
tes.
Fo
r
ex
am
p
le,
C
AR
T
is
s
aid
to
b
e
a
class
if
icatio
n
d
ec
is
io
n
tr
ee
if
it
is
u
s
ed
to
p
r
ed
ict
a
d
ataset
in
to
two
class
es
.
I
t
is
s
aid
to
b
e
a
r
eg
r
ess
io
n
d
ec
is
io
n
tr
ee
if
it
is
u
s
ed
to
p
r
ed
ict
a
n
u
m
e
r
ical
v
a
r
iab
le
.
I
t
h
as
s
o
m
e
ad
v
an
tag
es
s
u
ch
as
:
it
h
a
n
d
l
es
th
e
m
is
s
in
g
v
alu
es
an
d
u
s
es
th
e
co
s
t
co
m
p
lex
ity
p
r
u
n
in
g
to
r
e
m
o
v
e
t
h
e
u
n
r
eliab
le
b
r
an
ch
es f
r
o
m
th
e
d
ec
is
io
n
tr
ee
to
in
cr
ea
s
e
th
e
ac
c
u
r
ac
y
[
1
3
]
.
3
.
5
.
5
.
Ra
nd
o
m
f
o
re
s
t
R
an
d
o
m
Fo
r
est
(
R
F)
is
a
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
f
o
r
class
if
icatio
n
,
r
eg
r
ess
io
n
,
an
d
o
th
er
task
s
.
Dec
is
io
n
tr
ee
s
p
lit
ea
ch
n
o
d
e
u
s
in
g
th
e
b
es
t
am
o
n
g
th
e
attr
i
b
u
tes
wh
ile
R
F
s
p
lit
ea
ch
n
o
d
e
u
s
in
g
th
e
b
est
am
o
n
g
th
e
r
a
n
d
o
m
ly
ch
o
s
en
s
u
b
s
et
o
f
p
r
ed
icto
r
s
at
th
e
n
o
d
e.
Hen
ce
R
F
p
er
f
o
r
m
s
well
co
m
p
ar
ed
to
o
th
er
class
if
icatio
n
tech
n
i
q
u
es
s
u
ch
as
SVM
an
d
n
eu
r
al
n
etwo
r
k
.
B
esid
es
th
is
,
it
is
r
o
b
u
s
t
ag
ain
s
t o
v
er
f
itti
n
g
[
1
6
]
.
3
.
6
.
Resul
t
ev
a
lua
t
io
n
Af
ter
th
e
im
p
lem
en
tatio
n
o
f
t
h
e
class
if
icatio
n
tech
n
i
q
u
e
o
n
th
e
s
tu
d
en
ts
’
d
ataset,
th
e
r
esu
lts
o
f
t
h
e
class
if
ier
m
o
d
els
wer
e
ev
alu
ated
an
d
r
ev
iewe
d
to
g
et
th
e
v
i
ewp
o
in
t
o
f
th
e
r
esu
lt
an
d
f
in
d
th
e
b
est
p
r
ed
ictio
n
m
o
d
el.
T
h
is
s
tu
d
y
ev
alu
ated
m
o
d
els
u
s
in
g
f
o
u
r
co
m
m
o
n
ly
u
s
ed
p
er
f
o
r
m
an
ce
m
ea
s
u
r
e
m
etr
ics
s
u
ch
as
A
cc
u
r
a
cy
,
R
ec
a
ll
,
P
r
ec
is
io
n
,
an
d
F
1
(
F
-
mea
s
u
r
e)
wh
er
e
th
ese
m
atr
ices
wer
e
ca
lcu
lated
u
s
in
g
th
e
co
n
f
u
s
io
n
m
atr
ix
[
1
7
]
.
I
n
class
if
icatio
n
p
r
o
b
lem
s
,
g
o
o
d
ac
cu
r
ac
y
in
class
if
icatio
n
is
th
e
p
r
im
ar
y
co
n
ce
r
n
[
1
8
]
.
So
,
co
n
f
u
s
io
n
m
atr
ix
is
th
e
s
u
itab
le
m
eth
o
d
to
d
eter
m
in
e
th
e
ac
cu
r
ac
y
w
h
ich
is
s
h
o
w
n
in
T
ab
le
2
.
“
A
co
n
f
u
s
io
n
m
atr
ix
o
f
s
ize
n
x
n
ass
o
ciate
d
with
a
class
if
ier
s
h
o
ws
th
e
p
r
ed
icted
a
n
d
ac
tu
al
class
if
icatio
n
,
wh
er
e
n
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s
-
Valid
atio
n
(
C
V)
m
eth
o
d
is
ap
p
lied
.
M
o
d
els
ar
e
ev
alu
ated
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ased
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n
th
e
co
m
m
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n
ly
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s
ed
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m
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ce
m
ea
s
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etr
ics
s
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ch
as
A
cc
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r
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cy
,
R
ec
a
ll,
P
r
ec
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io
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d
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Fig
u
r
e
7
s
h
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th
e
p
er
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m
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ce
o
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f
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wh
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ig
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8
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%),
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A
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4
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d
NB
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9
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ataset
with
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ea
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ch
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s
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ig
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men
t.Cl
ick,
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h
a
t.Cl
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F
ile.
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lick,
F
o
r
u
m.
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lick,
S
ystem.
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lick,
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r
l.Cl
ick
a
n
d
Wiki.C
lick
as
in
d
ep
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d
en
t
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tu
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ests
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ik
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ests
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Fig
u
r
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8
s
h
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th
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p
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f
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m
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ce
o
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f
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els
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ased
o
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ta
wr
ap
p
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b
ased
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s
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Fro
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ig
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6
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wh
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e
s
am
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9
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in
with
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th
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u
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ests
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th
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o
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t
in
f
lu
en
cin
g
f
ea
tu
r
es
in
cr
ea
s
e
th
e
ac
cu
r
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f
th
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a
cc
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r
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wh
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t
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im
en
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io
n
ality
o
f
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ata
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o
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th
e
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Fig
u
r
e
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.
Per
f
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r
m
an
c
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o
f
5
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s
if
ier
m
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els with
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u
t
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ea
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n
m
eth
o
d
Fig
u
r
e
8
.
Per
f
o
r
m
an
c
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o
f
5
cla
s
s
if
ier
m
o
d
els with
f
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tu
r
e
s
elec
tio
n
m
eth
o
d
T
h
e
r
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lt
s
h
o
ws
th
at
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V
M
h
as
th
e
h
ig
h
est
a
cc
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r
a
cy
o
f
9
3
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4
%
th
a
n
o
th
e
r
class
if
ier
s
’
ac
cu
r
ac
ies
s
u
ch
as
KNN
(
8
7
.
8
8
%),
RF
(
8
4
.
8
5
%),
C
A
R
T
(
8
4
.
8
5
%),
a
n
d
N
B
(
6
9
.
7
0
%)
o
n
th
e
test
in
g
d
a
taset
b
ef
o
r
e
f
ea
tu
r
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s
elec
tio
n
m
eth
o
d
s
.
Af
ter
th
e
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
,
two
f
ea
tu
r
es
F
ile.
C
lick
an
d
S
ystem.
C
lick
is
f
o
u
n
d
to
b
e
th
e
m
o
s
t
i
n
f
lu
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g
f
ea
tu
r
e
s
f
o
r
th
e
p
r
ed
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n
m
o
d
el.
So
,
f
o
r
th
e
f
u
r
th
er
ex
p
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im
e
n
t,
F
ile.
C
lick
an
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ystem.
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lick
is
co
n
s
id
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ed
as
in
d
ep
en
d
e
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t
f
ea
tu
r
es
a
n
d
Gra
d
e
as
a
d
ep
en
d
en
t.
T
h
e
r
esu
lt
with
s
elec
ted
f
ea
tu
r
es
s
h
o
ws
th
at
S
V
M
an
d
C
A
R
T
h
av
e
th
e
s
am
e
ac
cu
r
ac
y
as
b
ef
o
r
e
f
ea
tu
r
e
s
elec
tio
n
.
Ho
wev
er
,
o
th
er
class
if
ier
s
s
u
ch
as
K
N
N
,
N
B
,
a
n
d
RF
s
h
o
w
d
if
f
er
e
n
t a
cc
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r
ac
y
r
esu
lts
.
5.
CO
NCLU
SI
O
N
T
h
e
f
in
d
in
g
o
f
th
is
r
esear
ch
s
u
g
g
ests
th
at
th
e
s
elec
ted
m
o
s
t
in
f
lu
en
cin
g
f
ea
t
u
r
es
in
cr
ea
s
e
th
e
ac
cu
r
ac
y
o
f
s
tu
d
en
ts
’
p
e
r
f
o
r
m
an
ce
p
r
e
d
ictio
n
m
o
d
el
i.e
.
t
h
e
r
e
is
a
s
tr
o
n
g
r
ela
tio
n
s
h
ip
b
et
w
ee
n
u
s
ers
’
o
n
lin
e
a
ctivities
a
n
d
t
h
eir
p
erfo
r
ma
n
ce
.
T
h
e
s
tu
d
y
h
elp
s
in
ea
r
ly
i
d
en
tific
atio
n
o
f
s
tu
d
e
n
ts
’
p
e
r
f
o
r
m
an
ce
a
n
d
c
o
u
r
s
e
-
r
elate
d
p
r
o
b
lem
s
th
at
th
ey
ca
n
p
lan
ea
r
ly
with
a
p
r
o
p
er
d
e
cisi
o
n
s
u
ch
as
im
p
r
o
v
em
en
t
o
f
teac
h
in
g
/lear
n
in
g
p
r
o
ce
s
s
es,
p
r
o
p
er
c
o
u
n
s
elin
g
t
o
th
e
wea
k
lear
n
e
r
s
.
As
a
r
esu
lt,
it
will
im
p
r
o
v
e
th
e
s
tu
d
en
ts
’
p
er
f
o
r
m
an
ce
a
n
d
r
ed
u
ce
th
e
n
u
m
b
er
o
f
s
tu
d
en
ts
’
d
r
o
p
o
u
t
in
th
e
co
u
r
s
e
wh
ich
co
n
s
eq
u
en
tly
h
el
p
s
in
th
e
ac
ad
em
ic
ac
h
iev
em
en
t
o
f
th
e
in
s
titu
tio
n
.
Fu
tu
r
e
wo
r
k
ca
n
b
e
im
p
lem
e
n
tin
g
t
h
e
co
n
ce
p
t
o
f
lear
n
in
g
an
aly
tics
(
L
A
)
,
wh
ich
f
o
cu
s
es
o
n
in
f
o
r
m
in
g
an
d
em
p
o
wer
in
g
i
n
s
tr
u
cto
r
s
an
d
lear
n
er
s
.
B
o
th
E
DM
an
d
L
A
ca
n
b
e
a
p
p
lied
to
ed
u
ca
tio
n
al
d
ata
to
g
et
m
ea
n
i
n
g
f
u
l
in
f
o
r
m
atio
n
f
o
r
th
e
im
p
r
o
v
em
e
n
t
o
f
th
e
teac
h
in
g
-
lear
n
in
g
p
r
o
ce
s
s
.
T
o
ac
h
i
ev
e
th
is
task
,
a
u
s
er
m
o
d
el
ca
n
b
e
d
ev
elo
p
ed
th
at
f
it
with
th
e
in
s
tr
u
ctio
n
al
d
esig
n
s
t
r
ateg
ies.
Dif
f
er
en
t
ty
p
es
o
f
lear
n
er
'
s
d
ata
r
elate
d
to
th
eir
b
ac
k
g
r
o
u
n
d
,
m
o
tiv
atio
n
,
lear
n
in
g
s
ty
les,
an
d
co
g
n
itio
n
ca
n
b
e
lin
k
ed
with
th
eir
lo
g
d
ata.
T
h
e
f
o
cu
s
ca
n
b
e
o
n
ex
p
lan
at
o
r
y
m
o
d
els th
at
h
ig
h
lig
h
t th
e
r
elatio
n
s
h
ip
b
etwe
en
th
ese
d
ata.
RE
F
E
R
E
NC
E
S
[
1
]
R.
S
Ba
k
e
r
a
n
d
K.
Ya
c
e
f,
“
Th
e
sta
te
o
f
e
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
in
g
in
2
0
0
9
:
A
re
v
iew
a
n
d
f
u
tu
re
v
isio
n
s
”
,
J
EDM
J
o
u
rn
a
l
o
f
E
d
u
c
a
t
io
n
a
l
D
a
ta
M
i
n
in
g
,
v
o
l.
1
,
n
o
.
1
,
p
p
.
3
-
1
6
,
2
0
0
9
.
[
2
]
S
.
B.
Ah
e
r
a
n
d
L.
M
.
R.
J.
Lo
b
o
,
“
Co
u
rse
re
c
o
m
m
e
n
d
e
r
sy
ste
m
in
e
-
lea
rn
in
g
”
.
I
n
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Co
mp
u
ter
S
c
ien
c
e
a
n
d
Co
mm
u
n
ica
ti
o
n
,
v
o
l
.
3
,
n
o
.
1
,
p
p
.
1
5
9
-
1
6
4
,
2
0
1
2
.
[
3
]
A.
Du
tt
,
M
.
A.
Ism
a
il
,
a
n
d
T.
He
r
a
wa
n
,
“
A sy
ste
m
a
ti
c
re
v
iew
o
n
e
d
u
c
a
ti
o
n
a
l
d
a
ta m
in
in
g
”
.
IEE
E
A
c
c
e
ss
,
v
o
l.
5
,
p
p
.
1
5
9
9
1
-
1
6
0
0
5
,
2
0
1
7
.
[
4
]
A.
Alg
a
rn
i,
“
Da
ta
m
in
in
g
in
e
d
u
c
a
ti
o
n
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Ad
v
a
n
c
e
d
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
7
,
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o
.
6
,
p
p
.
4
5
6
-
4
6
1
,
2
0
1
6
.
[
5
]
D.
Krp
a
n
a
n
d
S
.
S
ta
n
k
o
v
,
“
E
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
i
n
g
f
o
r
g
ro
u
p
in
g
stu
d
e
n
ts
i
n
e
-
lea
rn
in
g
sy
ste
m
”
,
In
Pro
c
e
e
d
in
g
s
o
f
th
e
Iti
2
0
1
2
3
4
th
I
n
ter
n
a
ti
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l
Co
n
fer
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n
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I
n
fo
rm
a
ti
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T
e
c
h
n
o
l
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y
In
ter
fa
c
e
s
,
p
p
.
2
0
7
-
2
1
2
,
2
0
1
2
.
[
6
]
C.
Ro
m
e
ro
a
n
d
S
.
Ve
n
t
u
ra
,
“
Ed
u
c
a
ti
o
n
a
l
d
a
ta
m
in
i
n
g
:
a
re
v
iew
o
f
t
h
e
sta
te
o
f
th
e
a
rt”,
I
EE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
y
ste
ms
,
M
a
n
,
a
n
d
Cy
b
e
rn
e
ti
c
s,
P
a
rt C
(A
p
p
li
c
a
ti
o
n
s
a
n
d
Rev
iews
)
,
v
o
l
.
40
,
n
o
.
6
,
p
p
.
6
0
1
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1
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0
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0
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7
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A.M
.
De
M
o
ra
is,
J.
M
.
Ara
u
jo
a
n
d
E
.
B.
C
o
sta
,
“
M
o
n
it
o
rin
g
stu
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t
p
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a
n
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e
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d
a
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lu
ste
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p
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2
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E)
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d
in
g
s
,
p
p
.
1
-
8,
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E,
2
0
1
4
.
[
8
]
S
.
Ra
n
a
a
n
d
R
.
G
a
rg
,
“
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v
a
lu
a
ti
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stu
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n
t’s
p
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rf
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rm
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stit
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te
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n
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a
lg
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h
m
s”
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ter
n
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t
io
n
a
l
J
o
u
rn
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l
o
f
A
p
p
li
e
d
En
g
i
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rin
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Res
e
a
rc
h
,
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l.
11
,
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o
.
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p
p
.
3
6
0
5
-
3
6
0
9
,
2
0
1
6
.
[
9
]
P
.
G
.
Esp
e
jo
,
S
.
Ve
n
tu
ra
a
n
d
F
.
He
rre
ra
,
“
A
su
rv
e
y
o
n
th
e
a
p
p
li
c
a
ti
o
n
o
f
g
e
n
e
ti
c
p
r
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g
ra
m
m
in
g
to
c
las
sifica
ti
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n
”
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
y
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ms
,
M
a
n
,
a
n
d
Cy
b
e
rn
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ti
c
s,
P
a
rt
C
(A
p
p
li
c
a
ti
o
n
s
a
n
d
Rev
iews
),
v
o
l.
40
,
n
o
.
2
,
p
p
.
1
2
1
-
1
4
4
,
2
0
1
0
.
[
1
0
]
C
Ro
m
e
ro
a
n
d
S
.
Ve
n
tu
ra
,
D
a
ta
m
in
in
g
i
n
e
d
u
c
a
ti
o
n
.
W
il
e
y
In
ter
d
isc
ip
li
n
a
ry
Rev
iews
:
Da
t
a
M
i
n
in
g
a
n
d
Kn
o
wled
g
e
Disc
o
v
e
ry
,
v
o
l
.
3
,
n
o
.
1
,
p
p
.
12
-
2
7
,
2
0
1
3
.
[
1
1
]
O.
M
a
imo
n
a
n
d
L
.
Ro
k
a
c
h
,
“
De
c
o
m
p
o
siti
o
n
m
e
th
o
d
o
l
o
g
y
fo
r
k
n
o
wle
d
g
e
d
isc
o
v
e
r
y
a
n
d
d
a
ta
m
in
in
g
”
,
I
n
Da
t
a
min
in
g
a
n
d
k
n
o
wle
d
g
e
d
isc
o
v
e
ry
h
a
n
d
b
o
o
k
,
p
p
.
9
8
1
-
1
0
0
3
,
S
p
ri
n
g
e
r,
Bo
sto
n
,
M
A,
2
0
0
5
.
[
1
2
]
S
.
S
.
Nik
a
m
,
“
A
c
o
m
p
a
ra
ti
v
e
st
u
d
y
o
f
c
las
sifica
ti
o
n
tec
h
n
i
q
u
e
s
i
n
d
a
ta
m
in
i
n
g
a
lg
o
r
it
h
m
s”
,
Ori
e
n
ta
l
J
o
u
rn
a
l
o
f
Co
mp
u
ter
S
c
ien
c
e
&
T
e
c
h
n
o
lo
g
y
,
v
o
l
.
8
,
n
o
.
1
,
p
p
.
1
3
-
1
9
,
2
0
1
5
.
[
1
3
]
S
.
K.
Ya
d
a
v
a
n
d
S
.
P
a
l,
“
Da
ta
m
in
in
g
:
A
p
re
d
ictio
n
fo
r
p
e
rfo
r
m
a
n
c
e
imp
ro
v
e
m
e
n
t
o
f
e
n
g
in
e
e
ri
n
g
st
u
d
e
n
ts
u
sin
g
c
las
sifica
ti
o
n
”
W
o
rld
o
f
Co
mp
u
te
r
S
c
ien
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e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
J
o
u
rn
a
l
W
CS
IT
,
v
o
l.
2
,
n
o
.
2
,
p
p
.
5
1
-
5
6
,
a
rXiv p
re
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ri
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t
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rX
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2
0
3
.
3
8
3
2
,
2
0
1
2
.
[
1
4
]
J.
H.
Kim
,
S
.
S
e
o
d
a
e
m
u
m
-
g
u
,
Y.
P
a
rk
,
J.
S
o
n
g
a
n
d
I
.
H.
Jo
,
“
P
re
d
ictin
g
st
u
d
e
n
ts'
lea
rn
in
g
p
e
rfo
r
m
a
n
c
e
b
y
u
sin
g
o
n
li
n
e
b
e
h
a
v
i
o
r
p
a
tt
e
rn
s
i
n
b
len
d
e
d
lea
rn
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g
e
n
v
ir
o
n
m
e
n
ts:
C
o
m
p
a
riso
n
o
f
two
c
a
se
s
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li
n
e
a
r
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n
d
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o
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r
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l”,
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ter
n
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l
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o
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o
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Ed
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c
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ti
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l
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n
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g
(E
DM
2
0
1
4
)
,
2
0
1
4
.
[
1
5
]
F.
Ah
m
a
d
,
N.
Ism
a
il
a
n
d
A.
A.
Az
iz,
“
Th
e
p
re
d
ict
io
n
o
f
stu
d
e
n
ts
’
a
c
a
d
e
m
ic
p
e
rfo
rm
a
n
c
e
u
sin
g
c
l
a
ss
ifi
c
a
ti
o
n
d
a
ta
m
in
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tec
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n
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q
u
e
s”
,
A
p
p
li
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d
M
a
t
h
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ma
ti
c
a
l
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c
ien
c
e
s
,
v
o
l.
9
,
n
o
.
1
2
9
,
p
p
.
6
4
1
5
-
6
4
2
6
,
2
0
1
5
.
[
1
6
]
Y.
Ab
u
b
a
k
a
r
,
a
n
d
N.B.
A
h
m
a
d
,
“
P
re
d
ictio
n
o
f
S
t
u
d
e
n
ts’
P
e
rfo
rm
a
n
c
e
in
E
-
Lea
rn
in
g
En
v
ir
o
n
m
e
n
t
Us
in
g
Ra
n
d
o
m
F
o
re
st”
,
In
ter
n
a
ti
o
n
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l
J
o
u
rn
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l
o
f
In
n
o
v
a
ti
v
e
Co
m
p
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ti
n
g
,
v
o
l.
7
,
n
o
.
2
,
p
p
.
1
-
5,
2
0
1
7
.
[
1
7
]
E.
A.
Am
rieh
,
T.
Ha
m
ti
n
i
a
n
d
I
.
Aljara
h
,
“
M
in
in
g
e
d
u
c
a
ti
o
n
a
l
d
a
ta
to
p
re
d
ict
S
t
u
d
e
n
t
’s
a
c
a
d
e
m
ic
p
e
rfo
rm
a
n
c
e
u
sin
g
e
n
se
m
b
le
m
e
th
o
d
s”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Da
t
a
b
a
se
T
h
e
o
ry
a
n
d
Ap
p
li
c
a
ti
o
n
,
v
o
l.
9
,
n
o
.
8
,
p
p
.
1
1
9
-
1
3
6
,
2
0
1
6
.
[
1
8
]
S.
Visa
,
B.
Ra
m
sa
y
,
A.L
.
Ra
les
c
u
a
n
d
E
.
Va
n
De
r
Kn
a
a
p
,
“
Co
n
f
u
sio
n
M
a
tri
x
-
b
a
se
d
F
e
a
tu
re
S
e
le
c
ti
o
n
”
,
M
AIC
S
,
710
,
1
2
0
-
1
2
7
,
2
0
1
1
.
[
1
9
]
A.
Ac
h
a
ry
a
a
n
d
D.
S
in
h
a
,
“
Ap
p
li
c
a
ti
o
n
o
f
fe
a
tu
re
se
lec
ti
o
n
m
e
t
h
o
d
s
in
e
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
i
n
g
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
l
ic
a
ti
o
n
s,
v
o
l
.
1
0
3
,
n
o
.
2
,
p
p
.
3
4
-
3
8
,
2
0
1
4
.
[
2
0
]
A.
M
u
e
e
n
,
B.
Zafa
r
a
n
d
U.
M
a
n
z
o
o
r,
“
M
o
d
e
li
n
g
a
n
d
P
re
d
ictin
g
S
tu
d
e
n
ts'
Ac
a
d
e
m
ic
P
e
rfo
rm
a
n
c
e
Us
in
g
Da
ta
M
in
i
n
g
Tec
h
n
iq
u
e
s”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
M
o
d
e
r
n
Ed
u
c
a
ti
o
n
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
1
,
n
o
.
1
1
,
pp
36
-
42
,
2
0
1
6
.
[
2
1
]
M
.
B.
Ku
rsa
a
n
d
W
.
R.
Ru
d
n
ick
i,
“
F
e
a
tu
re
se
lec
ti
o
n
with
th
e
Bo
r
u
ta
p
a
c
k
a
g
e
”
,
J
S
ta
t
S
o
ft
w
,
v
o
l
.
3
6
,
n
o
.
1
1
,
p
p
.
1
-
1
3
,
2
0
1
0
.
[
2
2
]
M
.
Al
-
S
a
lee
m
,
N.
Al
-
Ka
th
iry
,
S
.
Al
-
Os
imi
a
n
d
G
.
Ba
d
r,
“
M
in
in
g
e
d
u
c
a
ti
o
n
a
l
d
a
ta
to
p
re
d
ict
st
u
d
e
n
ts’
a
c
a
d
e
m
ic
p
e
rfo
rm
a
n
c
e
”
,
In
In
ter
n
a
ti
o
n
a
l
W
o
rk
sh
o
p
o
n
M
a
c
h
in
e
L
e
a
rn
i
n
g
a
n
d
Da
t
a
M
i
n
i
n
g
i
n
P
a
tt
e
rn
Rec
o
g
n
it
io
n
.
S
p
rin
g
e
r,
Ch
a
m
,
p
p
.
4
0
3
-
4
1
4
,
2
0
1
5
.
[
2
3
]
H.
Bh
a
v
sa
r
a
n
d
M
.
H.
P
a
n
c
h
a
l,
“
A
re
v
iew
o
n
su
p
p
o
r
t
v
e
c
to
r
m
a
c
h
in
e
fo
r
d
a
ta
c
las
sifica
ti
o
n
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
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