I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
201
8
,
p
p
.
19
~
25
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
8
i
1
.
pp
19
-
25
19
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Mar
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ev
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cc
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A
b
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-
In
In
tern
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l
is
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e
d
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w
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m
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d
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w
it
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a
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p
a
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rn
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Du
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e
w
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th
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d
a
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It
is
p
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in
m
a
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p
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sa
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d
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d
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a
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w
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ly
c
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.
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o
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su
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a
ta d
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t
ra
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si
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tec
h
n
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q
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s m
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s th
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ra
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it
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m
s ca
n
c
h
a
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v
e
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ti
m
e
so
d
a
ta f
ro
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th
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p
a
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m
a
y
b
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c
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m
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irr
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lev
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t
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f
a
lse
f
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th
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c
u
rre
n
t
p
re
d
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c
ti
o
n
.
F
o
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h
a
n
d
l
in
g
su
c
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v
a
ry
in
g
p
a
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rn
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f
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a
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c
o
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p
t
d
r
if
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m
in
in
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a
p
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h
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to
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c
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ra
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y
o
f
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las
s
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ica
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tec
h
n
iq
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s.
In
t
h
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a
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w
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p
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v
in
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th
e
a
c
c
u
ra
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y
o
f
c
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sif
ier.
T
h
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e
n
se
m
b
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c
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f
ier
is
a
p
p
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d
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3
d
if
f
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d
a
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stig
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f
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K
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:
A
cc
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C
las
s
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f
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Dr
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Fre
q
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atter
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©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
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d
E
n
g
i
n
e
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rin
g
a
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d
S
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.
Al
l
rig
h
ts
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se
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d
.
C
o
r
r
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s
p
o
nd
ing
A
uth
o
r
:
L
ee
n
a
A
Des
h
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an
d
e,
Dep
t o
f
C
o
m
p
u
ter
Sci.
&
E
n
g
g
,
K
L
U
n
iv
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s
it
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,
Gr
ee
n
Field
s
,
Vad
d
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w
ar
a
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Gu
n
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Di
s
tr
ict,
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.
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.
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m
ail:
d
es
h
p
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d
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leen
a2
7
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
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esear
ch
o
v
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last
f
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w
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a
d
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d
ev
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p
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m
a
n
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ata
m
i
n
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g
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r
ith
m
s
f
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co
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g
k
n
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w
led
g
e
u
n
d
er
l
y
i
n
g
t
h
e
d
at
a
[
1
]
,
[
2
]
.
T
h
ese
alg
o
r
ith
m
s
,
h
o
w
ev
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,
ar
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atasets
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k
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ated
at
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ate
s
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Data
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k
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ata
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On
e
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f
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ata
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s
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if
ts
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i.e
.
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ch
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ata
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r
ate
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o
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T
h
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h
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en
s
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in
c
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cla
s
s
i
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ier
s
lear
n
t
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ata
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ce
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ar
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s
ed
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lab
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s
ta
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ce
s
t
h
at
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lect
cu
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p
t
w
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ich
m
a
y
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if
f
er
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f
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m
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ld
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T
h
u
s
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f
o
r
h
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d
li
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g
d
r
if
t
s
i
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d
ata
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g
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h
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n
g
i
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n
m
e
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t
[
3
-
6
]
.
A
ls
o
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clas
s
if
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s
m
u
s
t
b
e
ab
le
to
d
etec
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s
o
b
s
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v
ed
[
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
201
8
:
19
–
25
20
Data
s
tr
ea
m
is
t
h
e
s
eq
u
en
ce
o
f
d
ata
in
s
ta
n
ce
s
{
x
t
,
y
t
}
f
o
r
ti
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e
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=1
,
2
,
3
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.
.
.
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T
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w
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er
e
x
is
s
et
o
f
attr
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u
tes
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d
y
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lab
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e
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at
a
s
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ata
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s
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n
ce
x
t
ar
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iv
e
s
,
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ier
C
p
r
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s
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s
lab
el.
A
f
ter
s
o
m
e
ti
m
e,
ac
tu
a
l
class
lab
el
y
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is
av
ailab
le
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d
is
u
s
ed
b
y
clas
s
i
f
ie
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f
o
r
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alu
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n
d
as
ad
d
itio
n
al
in
f
o
r
m
at
io
n
f
o
r
tr
ain
i
n
g
p
u
r
p
o
s
e.
T
h
is
tech
n
i
q
u
e
ca
lled
s
u
p
er
v
is
ed
lear
n
in
g
is
u
s
ed
b
y
m
o
s
t
o
f
d
ata
m
i
n
in
g
al
g
o
r
ith
m
s
.
Ho
w
e
v
er
co
n
s
tr
ain
t
s
ap
p
lied
b
y
s
tr
ea
m
d
ata
ar
e
n
o
t
w
ell
ad
d
r
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ed
b
y
t
h
is
tech
n
iq
u
e.
As
ti
m
e
e
lo
p
es,
th
e
co
n
ce
p
t
ab
o
u
t
w
h
ic
h
d
ata
is
co
llected
ch
an
g
es
o
v
er
ti
m
e.
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h
is
p
h
en
o
m
e
n
o
n
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also
ca
ll
ed
,
co
n
ce
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t
d
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if
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iv
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t
w
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ca
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ies
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s
u
d
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en
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r
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t
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n
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g
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ad
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al
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ir
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t
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r
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e
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o
u
r
ce
d
is
tr
ib
u
tio
n
S
o
f
d
ata
s
tr
ea
m
is
s
u
d
d
en
l
y
r
ep
lace
d
b
y
a
n
o
th
er
d
is
tr
ib
u
t
io
n
S
’
.
T
h
e
lat
er
t
y
p
e
o
f
d
r
if
t
is
ass
o
ciate
d
w
ith
s
lo
w
er
r
ate
o
f
ch
an
g
e
s
in
d
ata
s
tr
ea
m
s
.
T
y
p
icall
y
,
d
ata
in
s
ta
n
ce
s
f
r
o
m
d
if
f
er
en
t
s
o
u
r
ce
d
is
tr
ib
u
tio
n
s
s
tar
t
m
ix
i
n
g
,
wh
er
e
p
r
o
b
ab
ilit
y
o
f
o
b
s
er
v
i
n
g
d
ata
i
n
s
ta
n
ce
s
f
r
o
m
n
e
w
s
o
u
r
ce
d
is
tr
ib
u
tio
n
in
cr
ea
s
es
a
n
d
th
a
t
o
f
o
ld
d
is
tr
i
b
u
tio
n
d
ec
r
ea
s
es
o
v
er
t
i
m
e
M
u
ltip
le
al
g
o
r
ith
m
s
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
d
ea
lin
g
w
it
h
co
n
ce
p
t d
r
if
t
s
i
n
d
ata
s
tr
ea
m
s
.
Her
e
w
e
d
escr
ib
e
w
o
r
k
s
r
elate
d
to
o
u
r
s
tu
d
y
b
r
ief
l
y
.
Dr
if
t
d
etec
to
r
is
m
ec
h
a
n
is
m
u
s
ed
f
o
r
an
al
y
zi
n
g
d
ata
in
s
ta
n
c
es
an
d
tr
ig
g
er
i
n
g
alar
m
a
s
s
o
o
n
as
d
r
if
t
is
o
b
s
er
v
ed
.
T
h
e
tr
ig
g
er
in
d
ica
tes
n
ee
d
o
f
r
eb
u
ild
in
g
cla
s
s
i
f
ier
.
T
h
e
m
o
s
t
p
o
p
u
lar
d
r
if
t
d
etec
tio
n
is
Dr
i
f
t
Dete
ctio
n
Me
th
o
d
i
n
w
h
ic
h
p
r
ed
icted
lab
els
ar
e
co
m
p
ar
ed
w
it
h
ac
tu
al
lab
els
f
o
r
d
eter
m
i
n
in
g
clas
s
if
icatio
n
er
r
o
r
s
.
C
lass
i
f
i
ca
tio
n
er
r
o
r
is
m
o
n
ito
r
ed
to
ch
ec
k
i
f
it
f
all
s
b
e
y
o
n
d
ce
r
tain
t
h
r
es
h
o
ld
.
W
h
en
a
n
er
r
o
r
f
alls
b
ey
o
n
d
th
r
es
h
o
ld
,
alar
m
is
s
i
g
n
al
led
to
s
to
r
e
in
co
m
i
n
g
d
a
ta
in
s
ta
n
ce
s
i
n
to
a
b
u
f
f
er
.
W
h
en
alar
m
lev
e
l
is
r
ea
ch
ed
,
n
e
w
cla
s
s
i
f
ier
i
s
b
u
i
ld
o
n
d
ata
in
s
ta
n
ce
s
i
n
b
u
f
f
e
r
an
d
o
ld
class
i
f
ier
i
s
r
e
m
o
v
ed
.
C
o
n
ce
p
t
d
r
if
t
i
s
ad
ap
ted
in
to
s
y
s
te
m
w
h
e
n
s
y
s
t
e
m
is
u
p
d
ated
o
v
er
cu
r
r
en
t
co
n
ce
p
t.
T
h
e
p
o
p
u
lar
tech
n
iq
u
e
f
o
r
ac
co
m
m
o
d
ati
n
g
cu
r
r
en
t
co
n
ce
p
t
is
w
in
d
o
w
i
n
g
tec
h
n
iq
u
e.
T
h
is
tech
n
iq
u
e
h
elp
s
i
n
k
ee
p
in
g
s
elec
ted
d
a
ta
in
s
tan
ce
s
i
n
t
h
e
s
y
s
te
m
.
W
i
n
d
o
w
i
n
g
tec
h
n
iq
u
e
is
m
o
s
t
w
id
el
y
u
s
ed
,
s
i
n
ce
it
k
ee
p
s
m
o
s
t
r
ec
en
t
d
ata
in
s
tan
ce
s
w
h
ile
eli
m
i
n
ati
n
g
d
ata
in
s
ta
n
ce
s
b
el
o
n
g
i
n
g
to
o
ld
co
n
ce
p
ts
.
W
in
d
o
w
s
ize
i
s
co
m
m
o
n
tr
ad
e
-
o
f
f
d
u
e
to
th
e
f
ac
t
t
h
at
lar
g
er
w
i
n
d
o
w
s
ize
h
elp
s
in
k
ee
p
in
g
tr
ac
k
o
f
s
lo
w
e
r
ch
a
n
g
es,
b
u
t
f
ail
i
n
ca
s
e
o
f
s
u
d
d
en
ch
a
n
g
e
s
,
w
h
er
ea
s
s
m
al
ler
w
in
d
o
w
s
ize
ca
n
ad
a
p
t
s
u
d
d
en
d
r
if
ts
e
f
f
icie
n
tl
y
as
co
m
p
ar
ed
to
g
r
ad
u
al
ch
a
n
g
e
s
.
T
h
e
b
est
w
a
y
f
o
r
d
ea
lin
g
w
i
th
co
n
ce
p
t d
r
if
ts
i
n
d
ata
s
tr
ea
m
s
i
s
en
s
e
m
b
le
tec
h
n
iq
u
e
w
h
ic
h
is
a
s
et
o
f
co
m
p
o
n
en
t c
la
s
s
if
ier
,
v
o
tes
o
f
w
h
ic
h
ar
e
co
m
b
i
n
ed
to
p
r
ed
ict
class
lab
els.
E
n
s
e
m
b
le
cl
ass
i
f
ier
s
ar
e
b
est
f
o
r
d
ea
lin
g
w
it
h
c
h
an
g
e
s
in
d
ata
s
tr
ea
m
s
d
u
e
to
th
eir
m
o
d
u
lar
n
atu
r
e,
w
h
ich
allo
w
s
e
n
s
e
m
b
l
e
to
b
e
s
tr
u
ctu
r
ed
eith
er
b
y
r
e
tr
ain
i
n
g
co
m
p
o
n
e
n
t
class
i
f
ier
s
o
r
b
y
r
ep
lac
in
g
w
e
ak
est
cla
s
s
i
f
ier
b
y
r
ec
en
t
l
y
tr
a
in
ed
class
i
f
ier
o
r
b
y
u
p
d
atin
g
w
ei
g
h
ts
as
s
i
g
n
ed
to
co
m
p
o
n
e
n
t c
la
s
s
i
f
ier
s
d
ep
en
d
i
n
g
o
n
th
eir
r
esp
ec
ti
v
e
p
er
f
o
r
m
an
ce
s
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
Ma
n
y
al
g
o
r
ith
m
s
h
a
v
e
b
ee
n
d
ev
elo
p
ed
w
it
h
v
ar
iatio
n
s
in
b
asic
p
r
o
ce
s
s
in
g
tech
n
iq
u
e
o
f
en
s
e
m
b
le
[8
]
,
[
9
]
Min
k
u
et.
a
l.
[
1
0
]
p
r
o
p
o
s
ed
n
e
w
ap
p
r
o
ac
h
t
h
at
k
ee
p
s
d
if
f
er
en
t
en
s
e
m
b
le
s
f
o
r
d
ea
li
n
g
w
it
h
d
i
v
er
s
it
y
o
f
co
n
ce
p
ts
.
I
t
m
ai
n
tai
n
s
d
i
f
f
er
en
t
en
s
e
m
b
le
s
o
f
d
ata
s
tea
m
s
b
e
f
o
r
e
co
n
ce
p
t
d
r
if
t
a
n
d
af
ter
co
n
ce
p
t
d
r
if
t
i
n
o
r
d
er
to
k
ee
p
b
o
th
o
ld
an
d
n
e
w
co
n
ce
p
ts
in
t
h
e
s
y
s
te
m
Se
n
ar
at
n
e
et.
al.
[
1
2
]
p
r
o
p
o
s
ed
a
f
r
am
e
wo
r
k
f
o
r
d
eter
m
in
i
n
g
h
o
ts
p
o
t
o
f
t
w
itter
ac
ti
v
itie
s
an
d
d
etec
tin
g
d
r
if
ts
u
s
i
n
g
k
er
n
el
d
en
s
it
y
esti
m
atio
n
in
s
tr
ea
m
s
o
f
t
w
ee
ts
.
B
u
t
i
t
f
ails
i
n
d
eter
m
i
n
in
g
th
e
t
y
p
e
o
f
d
r
if
t
d
etec
ted
a
n
d
ta
k
in
g
m
ea
s
u
r
es
o
v
er
it.
W
e
o
v
er
co
m
e
t
h
i
s
p
r
o
b
le
m
b
y
in
te
g
r
atin
g
o
n
li
n
e
clas
s
i
f
ier
an
d
b
lo
ck
-
b
ased
clas
s
if
ier
w
i
th
in
s
i
n
g
le
en
s
e
m
b
le
f
o
r
r
ea
ctin
g
to
d
if
f
er
e
n
t
t
y
p
e
s
o
f
d
r
if
ts
e
f
f
icien
tl
y
.
.
T
o
m
ai
n
tai
n
m
i
n
i
m
u
m
n
u
m
b
er
o
f
e
n
s
e
m
b
les,
w
e
p
r
o
p
o
s
e
to
u
s
e
s
lid
in
g
w
i
n
d
o
w
tech
n
iq
u
e
w
h
ic
h
h
elp
s
in
k
ee
p
in
g
m
o
s
t
r
ec
e
n
t
d
ata
in
s
ta
n
c
es.
T
h
ese
d
ata
in
s
ta
n
ce
s
ar
e
u
s
ed
f
o
r
r
etr
ain
in
g
co
m
p
o
n
e
n
t
clas
s
i
f
ier
s
,
s
o
as
t
o
k
ee
p
en
s
e
m
b
le
u
p
d
ated
o
v
e
r
r
ec
en
t
co
n
ce
p
t.
In
[
1
3
]
,
[
1
4
]
au
t
h
o
r
s
p
r
o
p
o
s
ed
a
s
y
s
te
m
t
h
at
m
ai
n
tai
n
s
en
s
e
m
b
l
e
o
f
p
er
-
f
ea
t
u
r
e
clas
s
i
f
ier
s
.
O
n
e
class
i
f
ier
is
m
ai
n
tai
n
ed
f
o
r
a
s
i
n
g
le
f
ea
tu
r
e
o
f
a
p
ar
ticu
lar
class
.
S
u
ch
all
p
er
-
f
ea
tu
r
e
class
i
f
ier
s
o
f
a
class
ar
e
co
m
b
i
n
ed
f
o
r
all
class
es
m
a
k
in
g
it
h
ier
ar
ch
y
o
f
w
ei
g
h
ted
clas
s
if
ier
s
.
T
h
e
s
y
s
te
m
s
p
a
n
s
o
v
er
lar
g
e
m
e
m
o
r
y
s
p
ac
e
as
th
e
n
u
m
b
er
o
f
c
l
ass
es
in
cr
ea
s
e
s
a
n
d
th
er
eb
y
in
cr
ea
s
in
g
ti
m
e
o
v
er
h
ea
d
.
Ou
r
s
y
s
te
m
an
a
l
y
ze
s
f
ea
t
u
r
es
o
f
clas
s
f
o
r
ch
ec
k
i
n
g
o
u
t
f
ea
t
u
r
es
r
esp
o
n
s
ib
l
e
f
o
r
d
r
if
t,
if
a
n
y
a
n
d
f
o
r
u
p
d
ati
n
g
e
n
s
e
m
b
le
w
it
h
n
ec
e
s
s
ar
y
m
ea
s
u
r
es.
T
o
o
v
er
co
m
e
th
e
p
r
o
b
le
m
o
f
a
v
ailab
ilit
y
o
f
a
ctu
a
l
clas
s
lab
el
s
,
lear
n
i
n
g
tech
n
iq
u
e
is
ca
te
g
o
r
is
ed
in
to
t
w
o
t
y
p
e
s
:
o
n
li
n
e
lear
n
in
g
an
d
b
lo
ck
-
b
ased
lear
n
in
g
.
I
n
f
ir
s
t
ap
p
r
o
ac
h
,
class
if
ier
p
r
ed
ict
s
an
d
ev
al
u
ates
a
s
s
o
o
n
as
d
atai
n
s
ta
n
ce
i
s
av
ai
la
b
le.
W
h
er
ea
s
in
la
ter
ap
p
r
o
ac
h
,
b
lo
ck
s
o
f
d
ata
in
s
ta
n
ce
s
a
r
e
u
s
ed
f
o
r
ev
al
u
ati
n
g
class
i
f
ier
p
er
f
o
r
m
a
n
ce
.
L
ittl
e
s
t
o
n
e
et.
al.
[
1
5
]
p
u
t
f
o
r
w
ar
d
o
n
e
o
f
th
e
al
g
o
r
ith
m
s
f
o
r
o
n
li
n
e
lear
n
in
g
,
W
eig
h
ted
Ma
j
o
r
ity
Alg
o
r
it
h
m
w
h
ic
h
ag
g
r
eg
ate
s
p
r
ed
ictio
n
s
o
f
co
m
p
o
n
en
t
cla
s
s
i
f
ier
s
a
n
d
u
p
d
ates
w
eig
h
ts
o
f
clas
s
i
f
i
er
s
w
h
e
n
p
r
ed
icti
o
n
s
g
o
w
r
o
n
g
.
An
o
th
er
e
n
s
e
m
b
le
p
r
o
p
o
s
ed
b
y
Ko
tler
et.
al.
[
1
6
]
m
ain
tain
s
s
et
o
f
cla
s
s
i
f
ier
s
,
th
e
w
ei
g
h
ts
o
f
w
h
ic
h
ar
e
u
p
d
ates
in
cr
e
m
e
n
tal
l
y
a
f
ter
ea
ch
d
ata
in
s
ta
n
ce
.
On
ea
ch
m
is
cla
s
s
i
f
icatio
n
o
f
d
ata
in
s
ta
n
ce
,
t
h
e
w
eig
h
t
s
o
f
t
h
e
cl
ass
i
f
ier
s
m
ak
in
g
f
a
ls
e
p
r
e
d
ictio
n
s
ar
e
d
ec
r
em
e
n
ted
.
Me
m
o
r
y
co
n
s
tr
ain
t
is
o
n
e
a
m
o
n
g
m
an
y
ch
al
len
g
es
w
h
ile
h
an
d
lin
g
d
ata
s
tea
m
s
w
it
h
co
n
ce
p
t
d
r
if
t.
Ha
y
at
et
.
al.
[
1
7
]
p
r
o
p
o
s
ed
co
m
p
ac
t
clu
s
ter
in
g
tech
n
iq
u
e
to
o
v
er
co
m
e
th
i
s
p
r
o
b
lem
.
T
r
a
d
itio
n
all
y
,
ev
er
y
d
ata
in
s
ta
n
ce
o
f
b
elo
n
g
in
g
to
clu
s
ter
w
as
u
s
ed
f
o
r
ev
al
u
ati
n
g
th
e
clu
s
ter
.
T
h
e
p
r
o
p
o
s
ed
co
m
p
ac
t
cl
u
s
ter
i
n
g
alg
o
r
ith
m
u
s
es
o
n
l
y
n
ei
g
h
b
o
u
r
h
o
o
d
in
s
tan
ce
s
f
o
r
class
i
f
icatio
n
an
d
clu
s
ter
s
f
o
r
m
ed
f
r
o
m
u
n
clas
s
if
ied
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
C
o
n
ce
p
t D
r
ift I
d
en
tifi
ca
tio
n
u
s
in
g
C
la
s
s
if
ier E
n
s
emb
le
A
p
p
r
o
a
ch
(
Leen
a
Desh
p
a
n
d
e)
21
in
s
ta
n
ce
s
ar
e
co
m
p
ar
ed
w
it
h
c
lu
s
ter
s
o
f
clas
s
i
f
ied
in
s
ta
n
ce
s
f
o
r
ch
ec
k
in
g
ab
n
o
r
m
alit
y
an
d
d
etec
tin
g
d
r
if
t
s
.
Gao
et.
al.
[
1
8
]
p
r
o
p
o
s
ed
f
r
am
e
w
o
r
k
f
o
r
d
etec
tin
g
d
r
if
t
as
w
ell
as
n
e
w
e
m
er
g
i
n
g
clas
s
es
i
n
d
ata
s
tr
ea
m
s
u
s
i
n
g
ti
m
e
co
n
s
tr
ain
ts
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
m
ai
n
tai
n
s
b
u
f
f
er
o
f
i
n
s
t
an
ce
s
u
n
c
lass
if
ied
b
y
en
s
e
m
b
le
f
o
r
ce
r
tain
t
i
m
e
p
er
io
d
.
I
n
s
tan
ce
s
r
e
m
ai
n
i
n
g
u
n
clas
s
i
f
ied
a
f
ter
ti
m
e
l
i
m
it
e
x
p
ir
y
ar
e
co
n
s
id
er
ed
to
b
e
f
o
r
m
i
n
g
d
r
if
t
an
d
ar
e
f
u
r
t
h
er
an
a
l
y
ze
d
f
o
r
n
o
v
el
cl
a
s
s
e
v
o
lu
tio
n
.
E
v
o
l
u
tio
n
o
f
n
o
v
el
clas
s
es
i
s
an
o
t
h
er
ch
al
len
g
e
in
lear
n
i
n
g
f
r
o
m
d
ata
s
tr
ea
m
s
i
n
w
h
ic
h
n
e
w
class
es
e
m
er
g
e
o
v
er
ti
m
e
g
e
n
er
atin
g
t
h
e
n
ee
d
o
f
r
e
s
tr
u
ct
u
r
in
g
e
n
s
e
m
b
le
b
y
b
u
ild
in
g
n
e
w
clas
s
i
f
ier
f
o
r
n
e
w
clas
s
an
d
eli
m
in
at
in
g
th
e
w
ea
k
e
s
t
co
m
p
o
n
e
n
t
clas
s
if
ie
r
.
No
v
el
class
an
d
f
ea
t
u
r
e
ev
o
lu
t
io
n
h
a
v
e
b
ee
n
s
tu
d
ied
an
d
m
a
n
y
al
g
o
r
ith
m
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
d
ea
lin
g
w
it
h
th
e
s
e
is
s
u
e
s
[
1
9
-
2
1
]
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
f
o
r
h
a
n
d
lin
g
co
n
ce
p
t
d
r
i
f
ts
in
d
ata
s
tr
ea
m
s
u
s
es
en
s
e
m
b
le
clas
s
i
f
ier
tech
n
iq
u
e
f
o
r
b
u
ild
i
n
g
b
ase
c
lass
if
ier
s
an
d
u
s
i
n
g
t
h
e
m
f
o
r
clas
s
if
ic
atio
n
o
f
te
s
ti
n
g
d
ata.
T
h
e
s
y
s
te
m
b
u
i
ld
s
o
n
lin
e
class
i
f
ier
as
s
o
o
n
as
n
e
w
d
ata
in
s
ta
n
ce
is
av
a
ilab
le
an
d
wh
en
b
lo
ck
o
f
f
i
x
ed
n
u
m
b
er
o
f
d
ata
in
s
ta
n
ce
s
is
f
o
r
m
ed
,
b
lo
ck
b
ased
c
class
if
ier
is
d
ev
elo
p
ed
.
T
h
e
class
if
icatio
n
o
f
in
co
m
i
n
g
d
ata
in
s
ta
n
ce
is
d
o
n
e
u
s
i
n
g
w
ei
g
h
te
d
m
aj
o
r
it
y
o
f
b
ase
clas
s
i
f
ier
s
u
s
in
g
w
ei
g
h
tin
g
f
u
n
ctio
n
s
as:
W
h
er
e,
is
w
ei
g
h
t o
f
b
ase
clas
s
if
ier
at
ti
m
e
an
d
)
is
th
e
ac
cu
r
ac
y
o
f
class
if
ier
at
ti
m
e
.
T
h
e
ac
cu
r
ac
y
an
d
er
r
o
r
r
ates
ar
e
m
o
n
ito
r
ed
f
o
r
ea
ch
t
y
p
e
o
f
class
if
ier
co
n
tin
u
o
u
s
l
y
o
v
er
b
lo
ck
s
o
f
d
ata
in
s
ta
n
ce
s
u
s
in
g
E
r
r
o
r
R
ate
f
u
n
ctio
n
as:
W
h
er
e,
is
t
h
e
er
r
o
r
r
ate
o
f
cla
s
s
i
f
ier
o
n
r
ec
en
t
b
lo
ck
o
f
d
ata
in
s
tan
ce
s
a
n
d
is
th
e
p
r
o
b
ab
ili
t
y
g
iv
e
n
b
y
t
h
e
clas
s
i
f
ier
th
at
is
an
in
s
ta
n
ce
o
f
clas
s
.
As
th
e
v
al
u
e
o
f
er
r
o
r
r
ate
m
o
n
ito
r
in
g
cr
o
s
s
e
s
ce
r
tain
t
h
r
es
h
o
ld
,
d
r
if
t
is
d
etec
ted
.
T
h
ese
d
r
if
ts
a
r
e
an
al
y
ze
d
a
n
d
en
s
e
m
b
le
is
u
p
d
ated
ac
co
r
d
in
g
l
y
.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
I
n
o
u
r
ex
p
er
i
m
e
n
ts
,
w
e
ev
a
lu
ate
o
u
r
p
r
o
p
o
s
ed
en
s
e
m
b
le
th
at
co
m
b
i
n
es
o
n
l
in
e
cla
s
s
i
f
ier
an
d
b
lo
ck
-
b
ased
class
if
ier
.
W
e
i
m
p
le
m
en
ted
o
u
r
en
s
e
m
b
le
s
y
s
te
m
in
J
av
a.
T
h
e
ex
p
er
im
e
n
ts
wer
e
p
er
f
o
r
m
ed
o
n
co
m
p
u
ter
s
y
s
te
m
w
it
h
I
n
te
l C
o
r
e
i5
4
8
0
M
@
2
.
6
7
GHz
p
r
o
c
ess
o
r
an
d
4
.
0
0
GB
o
f
R
A
M.
W
e
test
ed
th
e
p
er
f
o
r
m
a
n
ce
o
f
en
s
e
m
b
le
w
it
h
s
i
n
g
le
co
m
p
o
n
en
t
cla
s
s
i
f
ier
s
.
O
u
r
en
s
e
m
b
le
u
s
ed
k
=5
co
m
p
o
n
e
n
t
cla
s
s
i
f
ier
s
;
N
B
Tr
ee
,
J4
8
,
Lo
g
is
tic,
R
a
n
d
o
m
F
o
r
est
an
d
B
a
g
g
in
g
.
T
h
e
s
ize
o
f
b
lo
ck
u
s
ed
f
o
r
all
co
m
p
o
n
e
n
t
clas
s
i
f
ier
s
a
n
d
en
s
e
m
b
le
w
a
s
eq
u
al
d
=1
0
0
as
th
i
s
s
ize
w
as
b
e
s
t
s
u
itab
le
f
o
r
m
o
r
e
ac
cu
r
ate
r
esu
lts
.
W
e
ev
al
u
ated
en
s
e
m
b
le
p
er
f
o
r
m
an
ce
f
o
r
d
i
f
f
er
en
t
s
izes
o
f
b
lo
ck
:
5
0
,
1
0
0
,
2
0
0
,
5
0
0
an
d
1
0
0
0
.
W
e
o
b
s
er
v
ed
th
at
t
h
e
s
ta
tis
tica
l
co
m
p
ar
i
s
o
n
s
o
f
p
er
f
o
r
m
a
n
ce
s
o
f
en
s
e
m
b
l
e
f
o
r
ea
c
h
o
f
ab
o
v
e
b
lo
ck
s
ize
g
i
v
es
b
etter
r
es
u
lt
s
in
ter
m
s
o
f
ac
c
u
r
ac
y
w
h
e
n
b
lo
ck
s
ize
w
as
1
0
0
.
Ho
w
e
v
er
B
lo
ck
s
ize
d
o
es
n
o
t
alter
en
s
e
m
b
le
ac
cu
r
ac
y
s
ig
n
i
f
ica
n
tl
y
,
b
u
t
b
lo
ck
s
ize
m
atter
s
i
n
ca
s
e
o
f
d
r
if
t
d
etec
tio
n
.
I
f
th
e
b
lo
ck
s
ize
is
lar
g
e
it
ig
n
o
r
es
d
r
if
t
s
th
at
last
ed
f
o
r
s
m
a
ll ti
m
e,
w
h
i
le
s
m
aller
b
lo
ck
s
ize
d
etec
ts
d
r
i
f
ts
ev
en
i
f
t
h
er
e
ar
e
b
lip
s
o
r
n
o
is
e
in
d
ata
s
tr
ea
m
s
.
R
ea
l
w
o
r
ld
d
ata
co
n
tai
n
n
o
p
r
ec
is
e
in
f
o
r
m
atio
n
ab
o
u
t
o
cc
u
r
r
en
ce
o
r
ty
p
e
o
f
d
r
if
ts
i
n
it.
So
it
is
p
r
ac
ticall
y
i
m
p
o
s
s
ib
le
to
test
th
e
d
esire
ac
cu
r
ac
y
in
ter
m
s
o
f
d
r
if
t
h
o
wev
er
a
m
a
n
u
al
d
r
if
t
is
to
b
e
in
s
er
ted
in
th
e
d
ata
to
ac
h
iev
e
t
h
e
tar
g
et.
So
w
e
d
ec
id
ed
to
u
s
e
p
u
b
licall
y
a
v
ailab
le
m
ac
h
in
e
lear
n
i
n
g
b
en
c
h
m
ar
k
d
atasets
g
at
h
er
ed
in
th
e
UC
I
r
ep
o
s
ito
r
y
[
2
2
]
th
at
s
i
g
n
i
f
ied
p
r
esen
ce
o
f
g
r
ad
u
al
d
r
i
f
ts
.
W
e
ev
alu
a
te
o
u
r
e
n
s
e
m
b
le
o
f
o
n
lin
e
clas
s
i
f
ier
an
d
b
lo
ck
-
b
as
ed
class
i
f
ier
a
g
ai
n
s
t
s
i
n
g
le
cla
s
s
i
f
ier
s
a
s
w
ell
/
W
e
ch
o
s
e
J
4
8
,
NB
T
r
ee
,
L
o
g
is
tic,
R
a
n
d
o
m
Fo
r
est
an
d
B
ag
g
in
g
as
co
m
p
o
n
en
t
class
if
ier
s
o
f
b
asic
en
s
e
m
b
le.
T
h
e
en
s
e
m
b
le
is
f
u
r
th
er
m
o
d
if
ied
f
o
r
lear
n
i
n
g
i
n
cr
e
m
e
n
tall
y
a
s
w
ell
a
s
i
n
b
l
o
ck
s
o
f
f
i
x
ed
s
ize.
Fro
m
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
o
f
p
r
o
p
o
s
ed
en
s
e
m
b
le
w
it
h
c
o
m
p
o
n
en
t c
la
s
s
i
f
ier
s
a
s
s
h
o
w
n
in
T
ab
le
1
,
w
e
ca
n
s
ee
th
at
en
s
e
m
b
le
i
m
p
r
o
v
es
t
h
e
ac
cu
r
ac
y
o
f
class
if
icatio
n
o
n
all
d
atasets
an
d
en
s
e
m
b
le
tak
es
eq
u
al
p
r
o
ce
s
s
in
g
ti
m
e.
T
ab
le
1.
P
er
f
o
r
m
a
n
ce
C
o
m
p
ar
is
o
n
b
et
w
ee
n
C
o
m
p
o
n
e
n
t
C
las
s
if
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I
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8
8
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8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
201
8
:
19
–
25
22
T
r
a
d
itio
n
al
ap
p
r
o
ac
h
o
f
cla
s
s
i
f
y
in
g
te
s
ti
n
g
d
ataset
a
g
ai
n
s
t
g
iv
en
tr
ai
n
in
g
d
ata
s
et
b
ec
o
m
e
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ataset
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s
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ased
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f
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ap
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is
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e.
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s
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ased
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th
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s
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w
n
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le
2
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Fro
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th
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x
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ts
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clu
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g
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1
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0
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s
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as b
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ize.
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2.
P
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t b
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En
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s
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ased
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t
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k
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n
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p
ar
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n
with
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m
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ased
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T
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3.
P
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s
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n
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o
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lin
e
a
n
d
b
lo
ck
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b
a
s
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tech
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iq
u
e
C
l
a
ssi
f
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e
r
A
c
c
u
r
a
c
y
C
o
mp
a
r
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s
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s
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n
g
:
O
n
l
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e
t
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c
h
n
i
q
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e
o
v
e
r
:
B
l
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c
k
-
b
a
se
d
t
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c
h
n
i
q
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d
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p
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m e
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W
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r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
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&
C
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I
SS
N:
2088
-
8708
C
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23
m
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2
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An
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ataset.
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s
e
c
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f
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ain
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o
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ataset
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ata
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t
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k
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:
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c
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if
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t e
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s
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c
h
a
n
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ai
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d
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taset
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if
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p
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n
t
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ata
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s
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iter
ativ
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y
f
o
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n
ex
t
b
lo
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s
o
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d
ata
s
tr
ea
m
s
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h
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s
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k
ee
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in
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n
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n
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th
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e
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s
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m
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Fig
u
r
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2
s
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o
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u
r
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1
.
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ar
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o
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et
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Fig
u
r
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.
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m
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4.
CO
NCLU
SI
O
N
Mo
s
t
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t
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p
licatio
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en
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ate
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ata
at
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ap
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ate,
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u
s
e
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tab
lis
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n
g
th
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n
ee
d
o
f
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p
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ata
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in
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g
tech
n
i
q
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es
f
o
r
cr
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s
en
s
iti
v
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ap
p
licatio
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s
.
Da
ta
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in
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is
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tio
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to
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.
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ata.
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en
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o
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r
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ad
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ter
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ito
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in
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c
h
ar
ac
ter
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lin
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clas
s
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f
icatio
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tech
n
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e
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d
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b
ased
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f
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tec
h
n
iq
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e
a
n
d
d
etec
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b
o
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t
y
p
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o
f
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f
f
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y
.
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r
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o
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r
s
y
s
te
m
a
n
al
y
ze
s
th
ea
t
tr
ib
u
tes
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
201
8
:
19
–
25
24
w
h
ic
h
ar
e
r
esp
o
n
s
ib
le
b
eh
i
n
d
th
e
ch
a
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sy
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b
ala
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ce
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ata
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m
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s
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u
tes
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f
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to
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e
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e
r
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ca
u
s
e
b
eh
i
n
d
th
e
d
r
i
f
t
[
2
3
]
.
Ou
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p
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p
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s
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s
y
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m
s
h
o
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s
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m
p
r
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v
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p
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f
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ce
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ile
d
etec
ti
n
g
b
o
th
k
i
n
d
s
o
f
d
r
if
ts
ef
f
ic
ien
t
l
y
.
Ho
w
e
v
er
o
u
r
w
o
r
k
f
o
cu
s
es o
n
o
f
f
l
in
e
s
tr
ea
m
i
n
g
d
ata
.
T
h
e
co
n
tr
ib
u
tio
n
o
p
en
s
s
ev
e
r
al
d
ir
ec
tio
n
s
f
o
r
r
esear
ch
s
tu
d
ies.
C
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r
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t
w
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k
s
i
n
d
a
ta
s
tr
ea
m
class
i
f
icatio
n
f
o
c
u
s
d
etec
tio
n
o
f
co
n
ce
p
t
d
r
if
t.
A
d
ap
tio
n
o
f
d
r
if
ts
to
th
e
s
y
s
te
m
lead
s
to
n
e
w
li
n
e
o
f
r
esear
c
h
.
An
i
n
ter
est
in
g
f
u
tu
r
e
w
o
r
k
w
o
u
ld
b
e
to
id
en
ti
f
y
e
v
o
lu
tio
n
o
f
n
e
w
co
n
ce
p
ts
f
o
r
o
n
li
n
e
s
tr
ea
m
i
n
g
d
ata
.
R
ec
e
n
t
tech
n
iq
u
es c
a
n
b
e
f
u
r
th
er
ex
te
n
d
ed
f
o
r
s
o
lv
i
n
g
n
o
v
elt
y
d
etec
tio
n
p
r
o
b
lem
.
RE
F
E
R
E
NC
E
S
[1
]
J.
Ha
n
,
“
Da
ta
M
in
in
g
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
iq
u
e
s”
,
M
o
rg
a
n
Ka
u
f
m
a
n
n
P
u
b
l
ish
e
rs
In
c
.
,
S
a
n
F
ra
n
c
isc
o
,
CA
,
USA
,
2
0
0
5
.
[2
]
N.
L
it
tl
e
sto
n
e
,
M
.
K.
W
a
rm
u
th
,
“
T
h
e
w
e
ig
h
ted
m
a
jo
rit
y
a
l
g
o
rit
h
m
”
,
In
f.
C
o
mp
u
t
.
1
0
8
(
2
)
(1
9
9
4
)
2
1
2
–
2
6
1
.
[3
]
M
.
M
.
M
a
u
s
u
d
,
J.
G
a
o
,
L
.
Kh
a
n
,
J.
Ha
n
,
a
n
d
B.
T
h
u
ra
isin
g
h
a
m
,
“
Clas
si
f
ica
ti
o
n
a
n
d
n
o
v
e
l
c
las
s
d
e
tec
ti
o
n
in
c
o
n
c
e
p
t
-
d
rif
ti
n
g
d
a
ta
stre
a
m
s
u
n
d
e
r
ti
m
e
c
o
n
stra
i
n
ts”
,
IEE
E
T
ra
n
s.
On
Kn
o
wled
g
e
a
n
d
Da
t
a
En
g
i
n
e
e
rin
g
,
V
o
l
.
2
3
,
N
o
.
6
,
p
p
.
8
5
9
-
8
7
3
,
J
u
n
e
2
0
1
1
.
[4
]
Bif
e
t,
G
.
Ho
l
m
e
s,
Rr.
Kirk
b
y
,
B.
P
f
a
h
rin
g
e
r,
“
M
OA
:
M
a
ss
iv
e
On
li
n
e
A
n
a
ly
si
s”
,
J.
M
a
c
h
.
L
e
a
rn
.
Re
s.
1
1
(
2
0
1
0
)
1
6
0
1
-
1
6
0
4
.
[5
]
J.
Ha
n
,
“
Da
ta
M
in
in
g
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
iq
u
e
s”
,
M
o
rg
a
n
Ka
u
f
m
a
n
n
P
u
b
l
ish
e
rs
In
c
.
,
S
a
n
F
ra
n
c
isc
o
,
CA
,
USA
,
2
0
0
5
.
[6
]
N.C.
Oz
a
,
S
.
J.
Ru
ss
e
ll
,
“
Exp
e
rime
n
ta
l
c
o
mp
a
riso
n
s
o
f
o
n
li
n
e
a
n
d
b
a
tc
h
v
e
rs
io
n
s
o
f
b
a
g
g
in
g
a
n
d
b
o
o
st
in
g
”
,
i
n
:
P
r
o
c
.
7
th
A
CM
S
IG
KD
D In
t.
Co
n
f
.
Kn
o
w
l.
Disc
.
Da
ta M
in
.
,
A
CM
P
re
ss
,
Ne
w
Yo
rk
,
NY
,
USA
,
2
0
0
1
.
[7
]
N.
S
tree
t,
Y.
Kim
,
“
A
stre
a
min
g
e
n
se
mb
le
a
lg
o
rith
m
(
S
EA
)
f
o
r
l
a
rg
e
-
sc
a
le
c
la
ss
if
ica
ti
o
n
”
,
i
n
:
P
r
o
c
.
7
t
h
A
CM
S
IG
KD
D In
t.
Co
n
f
.
Kn
o
w
l.
Disc
.
Da
ta M
in
.
,
A
CM
P
re
ss
,
Ne
w
Yo
rk
,
NY
,
USA
,
2
0
0
1
.
[8
]
N.
L
it
tl
e
sto
n
e
,
M
.
K.
W
a
rm
u
th
,
“
T
h
e
w
e
ig
h
ted
m
a
jo
rit
y
a
l
g
o
r
it
h
m
”
,
In
f.
C
o
mp
u
t
.
1
0
8
(
2
)
(1
9
9
4
)
2
1
2
–
2
6
1
.
[9
]
X
.
Zh
u
,
P
.
Z
h
a
n
g
,
X
.
L
in
,
a
n
d
Y.
S
h
i
,
“
A
c
ti
v
e
lea
rn
in
g
f
ro
m
stre
a
m
d
a
ta
u
sin
g
c
las
sif
i
e
r
e
n
se
m
b
le”
,
IEE
E
T
ra
n
s.
On
S
y
ste
ms
,
M
a
n
a
n
d
Cy
b
e
rn
e
ti
c
s
-
Pa
rt B
:
Cy
b
e
rn
e
ti
c
s
,
V
o
l.
4
0
,
No
.
6
,
p
p
.
1
6
0
7
-
1
6
2
1
,
De
c
.
2
0
1
0
.
[1
0
]
L
.
L
.
M
in
k
u
a
n
d
X
.
Ya
o
,
“
DD
D:
A
n
e
w
e
n
se
m
b
le
a
p
p
ro
a
c
h
f
o
r
d
e
a
li
n
g
w
it
h
c
o
n
c
e
p
t
d
rif
t”,
IEE
E
T
ra
n
s.
On
Kn
o
wled
g
e
a
n
d
D
a
ta
En
g
i
n
e
e
rin
g
,
V
o
l
.
2
4
,
N
o
.
4
,
p
p
.
6
1
9
-
6
3
3
,
A
p
ril
2
0
1
2
.
[1
1
]
X
.
Zh
u
,
P
.
Z
h
a
n
g
,
X
.
L
in
,
a
n
d
Y.
S
h
i
,
“
A
c
ti
v
e
lea
rn
in
g
f
ro
m
stre
a
m
d
a
ta
u
sin
g
c
las
sif
i
e
r
e
n
se
m
b
le”
,
IEE
E
T
ra
n
s.
On
S
y
ste
ms
,
M
a
n
a
n
d
Cy
b
e
rn
e
ti
c
s
-
Pa
rt B
:
Cy
b
e
rn
e
ti
c
s
,
V
o
l.
4
0
,
No
.
6
,
p
p
.
1
6
0
7
-
1
6
2
1
,
De
c
.
2
0
1
0
.
[1
2
]
J.
Ha
n
,
“
Da
ta
M
in
in
g
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
iq
u
e
s”
,
M
o
rg
a
n
Ka
u
f
m
a
n
n
P
u
b
l
ish
e
rs
In
c
.
,
S
a
n
F
ra
n
c
isc
o
,
CA
,
USA
,
2
0
0
5
.
[1
3
]
M
e
e
n
a
k
sh
i
A
n
u
ra
g
T
h
a
lo
r
,
S
h
ri
sh
a
il
a
p
a
P
a
ti
l
“
In
c
re
m
e
n
tal
L
e
a
rn
in
g
o
n
No
n
-
sta
ti
o
n
a
ry
Da
ta
S
trea
m
u
sin
g
En
se
m
b
le
A
p
p
ro
a
c
h
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
,
Vo
l
6
,
N
o
4
:
A
u
g
u
st 2
0
1
6
.
[1
4
]
B.
P
a
rk
e
r,
A
.
M
.
M
u
sta
f
a
,
a
n
d
L
.
Kh
a
n
,
“
N
o
v
e
l
c
la
ss
d
e
tec
ti
o
n
a
n
d
fea
tu
re
v
ia
a
ti
e
re
d
e
n
se
mb
le
a
p
p
ro
a
c
h
fo
r
stre
a
m
min
in
g
”
,
IEE
E
2
4
th
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
T
o
o
ls
w
it
h
A
rti
f
icia
l
In
telli
g
e
n
c
e
,
V
o
l.
1
6
,
No
.
8
,
p
p
.
1
1
7
1
-
1
1
7
8
,
N
o
v
.
2
0
1
2
.
[1
5
]
M
a
im
o
n
,
L
.
Ro
k
a
c
h
(Ed
s.),
“
Da
ta
M
in
i
n
g
a
n
d
Kn
o
w
led
g
e
Disc
o
v
e
r
y
H
a
n
d
b
o
o
k
”
,
2
n
d
e
d
.
,
S
p
ri
n
g
e
r,
2
0
1
0
.
[1
6
]
L
.
I.
Ku
n
c
h
e
v
a
,
“
Cla
ss
if
ier
e
n
se
mb
les
fo
r
c
h
a
n
g
i
n
g
e
n
v
iro
n
me
n
t
s”
,
in
:
P
ro
c
.
5
th
M
C
S
In
t
.
W
o
rk
sh
o
p
o
n
M
u
lt
.
Clas
s.
S
y
st.,
LNCS
,
v
o
l.
3
0
7
7
,
S
p
rin
g
e
r,
2
0
0
4
.
[1
7
]
M
.
Z.
Ha
y
a
t
a
n
d
M
.
R.
Ha
sh
e
m
i
,
“
DCT
Ba
se
d
Ap
p
ro
a
c
h
fo
r
D
e
tec
ti
n
g
No
v
e
lt
y
a
n
d
Co
n
c
e
p
t
Dr
if
t
in
d
a
t
a
stre
a
ms
”
,
IEE
E
C
o
n
f
e
re
n
c
e
o
n
S
o
f
t
Co
m
p
u
ti
n
g
a
n
d
P
a
tt
e
rn
Re
c
o
g
n
it
io
n
,
p
p
.
3
7
3
-
3
7
8
,
De
c
.
2
0
1
0
.
[1
8
]
M
.
M
.
M
a
u
s
u
d
,
J.
G
a
o
,
L
.
Kh
a
n
,
J.
Ha
n
,
a
n
d
B.
T
h
u
ra
isin
g
h
a
m
,
“
Clas
si
f
ica
ti
o
n
a
n
d
n
o
v
e
l
c
las
s
d
e
tec
ti
o
n
in
c
o
n
c
e
p
t
-
d
rif
ti
n
g
d
a
ta
stre
a
m
s
u
n
d
e
r
ti
m
e
c
o
n
stra
in
ts”
,
IEE
E
T
ra
n
s.
On
Kn
o
wled
g
e
a
n
d
Da
t
a
En
g
i
n
e
e
rin
g
,
V
o
l
.
2
3
,
N
o
.
6
,
p
p
.
8
5
9
-
8
7
3
,
J
u
n
e
2
0
1
1
.
[1
9
]
M
.
M
.
M
a
su
d
,
Q.
C
h
e
n
,
L
.
Kh
a
n
,
C.
C.
A
g
g
a
r
w
a
l,
J.
G
a
o
,
J.
Ha
n
,
A
.
S
riv
a
sta
v
a
,
a
n
d
N.C.
Oz
a
,
“
Clas
sif
ica
ti
o
n
a
n
d
A
d
a
p
ti
v
e
No
v
e
l
Clas
s
De
t
e
c
ti
o
n
o
f
F
e
a
tu
re
Ev
o
lv
in
g
Da
t
a
S
tre
a
m
s”
,
IEE
E
T
ra
n
s.
On
Kn
o
wled
g
e
a
n
d
Da
t
a
En
g
i
n
e
e
rin
g
,
V
o
l.
2
5
,
N
o
.
7
,
p
p
.
1
4
8
4
-
1
4
9
6
,
J
u
ly
2
0
1
3
.
[2
0
]
M
.
M
.
M
a
su
d
,
J.
G
a
o
,
L
.
Kh
a
n
,
J.
Ha
n
a
n
d
B
.
T
h
u
ra
isin
g
h
a
m
,
“
In
teg
ra
ti
n
g
No
v
e
l
Clas
s
D
e
tec
ti
o
n
w
it
h
Clas
sif
ic
a
ti
o
n
o
f
Co
n
c
e
p
t
Drif
ti
n
g
Da
ta S
trea
m
s”
,
ECM
L
,
S
p
rin
g
e
r/P
KD
D
,
p
p
.
7
9
-
9
4
,
2
0
1
0
.
[2
1
]
M
.
M
.
M
a
su
d
,
Q.
Ch
e
n
,
L
.
Kh
a
n
,
C.
C.
A
g
g
a
r
w
a
l,
J.
Ga
o
,
J.
Ha
n
,
a
n
d
B.
T
h
u
ra
isin
g
h
a
m
,
“
Ad
d
re
ss
in
g
C
o
n
c
e
p
t
-
Evo
lu
ti
o
n
in
Co
n
c
e
p
t
Dr
if
t
i
n
g
D
a
ta
S
tre
a
ms
”
,
IEE
E
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
d
a
ta m
in
in
g
,
p
p
.
9
2
9
-
9
3
4
,
2
0
1
0
.
[2
2
]
F
ra
n
k
,
A
.
As
u
n
c
io
n
,
UCI m
a
c
h
in
e
lea
rn
in
g
re
p
o
sit
o
ry
2
0
1
0
.
<
h
tt
p
:/
/arc
h
iv
e
.
ics
.
u
c
i.
e
d
u
/m
l/
d
a
tas
e
ts>
[2
3
]
F
a
tm
a
K
a
re
m
,
M
o
u
n
ir
Dh
ib
i
,
A
rn
a
u
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Evaluation Warning : The document was created with Spire.PDF for Python.