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eh
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l
.
[
1
4
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h
av
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ted
R
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an
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ased
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s
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So
o
d
[
1
5
]
p
r
o
p
o
s
ed
a
m
e
tr
ic
f
r
a
m
e
w
o
r
k
f
o
r
m
a
k
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n
g
t
h
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cisi
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a
m
o
n
g
r
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g
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d
m
ai
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te
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an
ce
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ca
lcu
late
s
R
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i
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g
r
eq
u
ir
e
m
e
n
t c
o
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it
h
‘
d
ef
ec
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s
t’
,
‘
Fa
u
l
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o
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t
’
as r
ee
n
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n
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g
m
etr
ic
s
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b
u
t h
is
ap
p
r
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ac
h
ig
n
o
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s
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ep
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in
r
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g
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ec
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k
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r
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g
m
o
d
el
s
h
a
v
e
also
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een
g
iv
e
n
b
y
v
ar
io
u
s
au
t
h
o
r
s
[
1
6
-
22
]
.
3.
M
E
T
H
O
DO
L
O
G
Y
Data
s
et
co
n
s
is
t
o
f
o
p
en
s
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u
r
ce
J
A
V
A
p
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ts
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f
d
if
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en
t
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ize
s
.
Dio
m
id
is
[
2
3
]
d
er
iv
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d
C
KJ
M
(
C
h
id
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m
b
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n
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Ke
m
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av
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Me
tr
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r
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ed
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ed
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KJ
M
v
er
-
1
.
9
[
2
4
]
f
o
r
j
av
a
co
d
e
an
a
l
y
s
i
s
.
C
K
m
etr
ics
ar
e
m
ea
s
u
r
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f
o
r
all
t
h
e
j
av
a
p
r
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j
ec
ts
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r
class
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t
h
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j
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a
p
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t,
s
tatis
tical
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th
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ar
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asic
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m
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f
t
h
e
p
r
o
j
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t
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T
A
C
MP
)
[
2
5
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.
T
o
tal
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ea
n
co
m
p
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T
MC)
is
av
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a
g
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m
p
le
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o
f
all
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t’
s
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A
C
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.
Dec
is
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ak
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ased
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n
t
h
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p
r
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t
h
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h
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n
tr
ee
.
R
ap
id
Min
er
s
t
u
d
io
v
er
-
7
.
1
[
2
6
]
is
u
s
ed
to
m
o
d
el
th
e
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ec
i
s
io
n
tr
ee
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d
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ets.
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ize
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io
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r
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ib
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r
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r
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s
ar
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p
r
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e
i
n
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atu
r
e
[
2
7
,
28]
.
P
r
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ctio
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ased
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n
th
e
r
u
les b
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iv
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ated
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1
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av
a
p
r
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ts
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T
r
ain
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ata
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tain
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SIZ
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L
O
C
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o
tal
A
v
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e
C
o
m
p
lex
i
t
y
o
f
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d
u
le
s
o
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th
e
p
r
o
j
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t
an
d
ca
teg
o
r
y
a
s
attr
ib
u
tes.
T
h
e
ca
te
g
o
r
y
attr
ib
u
te
i
s
a
lab
el
an
d
i
s
m
ea
s
u
r
e
d
o
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th
e
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asi
s
o
f
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MC
a
n
d
s
i
ze
.
An
o
th
er
Mo
d
el
d
ataset
co
n
tai
n
s
5
J
av
a
p
r
o
j
ec
ts
o
n
w
h
ic
h
p
r
ed
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s
w
i
ll b
e
ap
p
lied
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e
p
r
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p
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s
ed
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o
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ith
m
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s
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ted
w
h
ic
h
ca
lcu
la
te
th
e
co
m
p
lex
it
y
m
e
tr
ic
f
o
r
o
p
en
s
o
u
r
ce
s
o
f
t
w
ar
e
an
d
u
s
e
R
ap
id
M
i
n
er
to
o
l to
p
r
ed
ict
th
e
m
ain
ten
a
n
c
e
an
d
r
ee
n
g
i
n
ee
r
in
g
r
eq
u
ir
e
m
e
n
t.
I
n
p
u
t: Op
en
s
o
u
r
ce
J
A
V
A
p
r
o
j
ec
ts
,
Ou
tp
u
t: Stati
s
tical
C
o
m
p
le
x
it
y
Me
asu
r
es a
n
d
R
ee
n
g
i
n
ee
r
in
g
p
r
ed
ictio
n
s
1)
C
o
n
s
id
er
2
0
Op
en
s
o
u
r
ce
J
A
VA
p
r
o
j
ec
ts
2)
A
p
p
l
y
C
KJ
M
Me
tr
ic
to
o
l
to
ca
lcu
late
B
asic
Me
tr
ic
s
et
o
f
C
K
m
e
tr
ic
f
o
r
ea
ch
m
o
d
u
le
o
f
ev
er
y
s
i
n
g
le
J
A
V
A
p
r
o
j
ec
t
3)
P
er
f
o
r
m
s
tatis
t
ical
an
al
y
s
is
to
ca
lcu
late
to
tal
a
v
er
ag
e
c
o
m
p
le
x
it
y
o
f
m
o
d
u
les o
f
p
r
o
j
ec
t
[
T
AC
MP
]
.
4)
C
alcu
late
to
tal
Me
an
co
m
p
le
x
it
y
[
T
MC]
f
o
r
all
p
r
o
j
ec
ts
5)
An
al
y
ze
th
e
co
r
r
elatio
n
b
et
w
e
e
n
s
i
ze
a
n
d
t
o
tal
av
er
ag
e
co
m
p
lex
it
y
6)
Use
R
ap
id
Min
er
s
t
u
d
io
to
i
m
p
o
r
t D
ata
Sets
7)
A
p
p
l
y
‘
s
e
lect
attr
ib
u
te
‘
o
p
er
ato
r
o
n
im
p
o
r
ted
d
ata
to
s
elec
t Size
an
d
T
MC a
s
attr
ib
u
tes.
8)
Desig
n
R
o
les to
ap
p
ly
p
r
ed
icti
o
n
s
9)
Af
ter
Desi
g
n
i
n
g
R
o
les,
ap
p
l
y
Dec
is
io
n
tr
ee
o
p
er
ato
r
o
n
T
r
ai
n
in
g
d
ata
s
tr
ea
m
10)
Use
A
p
p
l
y
Mo
d
el
o
p
er
ato
r
t
o
ap
p
ly
p
r
ed
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n
to
Mo
d
el
Data
s
et
11)
E
x
ec
u
te
t
h
e
d
esi
g
n
ed
P
r
o
ce
s
s
to
g
et
th
e
p
r
ed
icted
r
esu
lt
4.
DIS
CU
SS
I
O
N
Ou
r
d
ataset
co
n
s
i
s
t
o
f
2
0
o
p
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n
s
o
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r
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J
av
a
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.
Fo
r
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r
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t,
th
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asic
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et
o
f
C
K
m
etr
ic
s
ar
e
g
en
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ated
u
s
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C
KJ
M.
T
ab
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p
r
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m
ea
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r
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m
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o
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m
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T
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p
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T
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m
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T
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m
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m
o
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all
t
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les.
Evaluation Warning : The document was created with Spire.PDF for Python.
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r
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ata
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e
t.
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n
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g
n
,
p
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ar
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e
R
o
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p
ar
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ter
s
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h
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t
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ar
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r
eq
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e
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t
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e
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D
t
h
at
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s
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it
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n
o
t
b
e
a
p
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o
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th
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n
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l
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s
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.
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ter
d
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n
i
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g
t
h
e
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les,
d
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is
io
n
tr
ee
o
p
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r
is
s
el
ec
ted
an
d
ad
d
ed
to
th
e
tr
ai
n
i
n
g
d
ata
s
tr
ea
m
.
T
h
is
d
ec
is
io
n
tr
ee
o
p
er
ato
r
g
en
er
ates
a
m
u
lti
w
a
y
d
ec
i
s
io
n
tr
ee
.
R
ap
id
Min
er
u
s
es
th
e
C
4
.
5
alg
o
r
ith
m
in
o
r
d
er
to
o
b
tain
m
u
lt
i
w
a
y
d
ec
is
io
n
tr
ee
.
T
h
e
in
p
u
t
to
t
h
e
d
ec
is
io
n
tr
ee
is
o
u
r
tr
ain
i
n
g
d
ata
s
et
co
n
s
is
tin
g
o
f
1
5
p
r
o
j
e
cts.
T
h
e
o
u
tp
u
t o
f
t
h
e
d
ec
is
io
n
tr
ee
is
clas
s
if
icatio
n
m
o
d
el
t
h
at
ca
n
b
e
ap
p
lied
to
th
e
n
e
w
d
ataset
(
m
o
d
el
d
ata
in
o
u
r
ca
s
e)
f
o
r
p
r
ed
ictio
n
.
I
n
Dec
is
io
n
tr
ee
p
ar
a
m
eter
s
,
w
e
s
elec
ted
cr
iter
io
n
as
Gin
i
in
d
e
x
.
R
etr
ie
v
e
o
p
er
ato
r
o
f
d
ata
m
o
d
el
is
co
n
n
ec
ted
w
i
th
t
h
e
r
o
le.
T
h
e
p
ar
a
m
eter
o
f
t
h
is
r
o
le
‘
ca
te
g
o
r
y
’
is
s
e
t
to
b
e
p
r
ed
ic
ted
.
A
s
s
h
o
w
n
i
n
F
ig
u
r
e
2
,
t
h
e
o
u
tp
u
t
o
f
d
ec
is
io
n
o
p
er
ato
r
is
co
n
n
ec
te
d
to
th
e
m
o
d
el
in
p
u
t
o
f
th
e
A
p
p
l
y
m
o
d
el
o
p
er
ato
r
.
T
h
e
m
o
d
el
d
ata
is
co
n
n
ec
t
ed
to
th
e
u
n
lab
elled
d
ata
in
p
u
t
o
f
A
p
p
l
y
m
o
d
el
o
p
er
ato
r
.
T
h
e
m
o
d
el
as a
n
in
p
u
t to
th
e
A
p
p
l
y
m
o
d
el
is
ap
p
lied
to
th
e
m
o
d
el
d
ata
at
an
u
n
la
b
elled
in
p
u
t.
T
h
e
ap
p
ly
m
o
d
el
ap
p
lies
t
h
e
tr
ain
i
n
g
m
o
d
el
to
th
e
m
o
d
el
d
ata
to
p
r
ed
ict
th
e
v
alu
e
o
f
t
h
e
attr
ib
u
te
th
at
i
s
w
it
h
‘
ca
te
g
o
r
y
’
attr
ib
u
te.
T
h
e
p
r
ed
ictio
n
is
ap
p
lie
d
to
th
e
m
o
d
el
d
ata.
A
p
p
l
y
m
o
d
el
p
r
o
d
u
ce
s
t
w
o
o
u
tp
u
ts
.
O
n
e
is
lab
eled
d
ata
th
at
is
w
h
en
tr
ai
n
i
n
g
d
ata
is
a
p
p
lied
to
m
o
d
el
d
ata,
th
e
attr
ib
u
te
w
ith
p
r
ed
ictio
n
r
o
le
is
ad
d
ed
to
th
e
m
o
d
el
d
ata.
T
h
is
attr
ib
u
te
s
to
r
es
t
h
e
p
r
ed
icted
v
alu
es
o
f
t
h
e
lab
e
led
attr
ib
u
te
u
s
in
g
th
e
g
i
v
en
tr
ain
ed
m
o
d
el.
An
o
t
h
er
o
u
tp
u
t
o
f
ap
p
l
y
m
o
d
el
o
p
er
ato
r
is
m
o
d
el.
T
h
e
tr
ai
n
i
n
g
m
o
d
el
th
at
w
a
s
i
n
p
u
t
is
p
ass
ed
w
it
h
o
u
t c
h
an
g
es to
t
h
e
o
u
tp
u
t p
o
r
t.
Fin
a
ll
y
,
e
x
ec
u
t
e
th
e
d
esig
n
ed
s
ce
n
ar
io
.
5.
RE
SU
L
T
E
x
ec
u
t
in
g
t
h
e
d
esi
g
n
,
d
ec
is
io
n
tr
ee
m
o
d
el
as
s
h
o
w
n
i
n
F
ig
u
r
e
3
w
ill
b
e
g
en
er
ated
a
n
d
ca
n
b
e
v
ie
w
ed
b
y
clic
k
in
g
o
n
t
h
e
tr
ee
tab
.
A
d
ec
is
io
n
tr
ee
i
s
a
co
llectio
n
o
f
n
o
d
es
an
d
leav
e
s
.
No
d
es
ar
e
r
ep
r
esen
ted
as
g
r
a
y
o
v
al
s
h
ap
es.
No
d
e
s
ar
e
th
e
a
ttrib
u
tes
t
h
at
s
er
v
e
as
g
o
o
d
p
r
ed
icto
r
.
On
th
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w
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l
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13
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[7
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[9
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1
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L
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W
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“
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2
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.
[1
3
]
Ch
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.
R.
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[1
4
]
Ra
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l
.
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0
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.
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5
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o
o
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S
.
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o
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sis,
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0
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tt
p
:
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.
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6
]
W
o
o
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S
.
,
e
t
a
l
.
,
“
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terc
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ro
c
.
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ti
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fer
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M
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in
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-
99)
,
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g
la
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s
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mito
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Co
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u
ter
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c
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p
.
3
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1
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1
9
9
9
.
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7
]
R.
Ka
z
m
a
n
,
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t
a
l
.
,
“
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q
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ts
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d
Re
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g
M
o
d
e
s:
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II,
”
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c
.
5
th
W
o
rk
in
g
Co
n
fer
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n
Rev
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rs
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En
g
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g
(
W
CR
E
-
98)
,
Ho
n
o
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,
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,
L
o
s
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m
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:
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Co
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u
ter
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c
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,
p
p
.
1
5
4
-
1
6
3
,
1
9
9
8
.
[1
8
]
E.
J.
By
rn
e
,
“
A
c
o
n
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e
p
tu
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l
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n
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a
ti
o
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f
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in
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g
,
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-
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3
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9
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2
.
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9
]
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n
g
X
.
,
e
t
a
l
.
,
“
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c
y
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0
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o
n
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p
p
.
1
-
5
,
2
0
0
5
.
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0
]
S
u
X
.
,
e
t
al
.
,
“
P
a
ra
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g
m
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f
leg
a
c
y
s
y
st
e
m
s,”
Pro
c
.
o
f
IEE
E
In
ter
n
a
ti
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l
Co
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fer
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n
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S
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ms
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M
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n
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,
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p.
4
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-
4
0
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8
,
2
0
0
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.
[2
1
]
M
u
rp
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y
G
.
C
.
a
n
d
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in
D.
,
“
Re
e
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m
o
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se
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d
y
,
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E
Co
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u
ter
,
v
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l
/i
ss
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e
:
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(
8
)
,
p
p
.
26
-
3
6
,
1
9
9
7
.
[2
2
]
Ch
u
n
g
S
.
,
“
S
e
rv
ice
-
Orie
n
ted
S
o
f
t
w
a
r
e
Re
e
n
g
in
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g
:
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o
S
R,
”
P
ro
c
.
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S
2
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Ha
w
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ter
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S
y
ste
ms
S
c
ien
c
e
,
W
a
ik
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lo
a
,
Big
Isla
n
d
,
HI,
USA
.
p
p
.
1
7
2
,
2
0
0
7
.
[2
3
]
S
p
in
e
ll
is D.
,
“
T
o
o
l
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rit
in
g
:
A
f
o
rg
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tt
e
n
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rt?”
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S
o
ft
w
a
re
,
v
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l/
is
su
e
:
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2
(
4
)
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p
p
.
9
-
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,
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0
0
5
.
[2
4
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CKJ
M
T
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o
l
W
e
b
site,
A
v
a
il
a
b
le:
h
tt
p
:
//
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w
.
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r/sw
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h
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l
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]
S
in
g
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J
.
,
e
t
a
l
.
,
“
Id
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ti
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g
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m
mu
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Au
t
o
ma
ti
o
n
(
ICCCA)
,
G
re
a
ter No
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a
,
In
d
ia,
p
p
.
9
3
1
-
9
3
4
,
2
0
1
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.
[2
6
]
Ra
p
id
M
in
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r
T
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o
l
W
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b
site Av
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il
a
b
le :
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tp
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