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Science
Vo
l.
3
6
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3
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Dec
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er
20
24
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1884
J
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DNA
m
icro
a
rra
y
p
ro
sta
te
c
a
n
c
e
r
d
iag
n
o
sis
sy
ste
m
s
a
re
wid
e
ly
u
se
d
,
a
n
d
h
y
b
rid
fe
a
tu
re
se
lec
ti
o
n
m
e
th
o
d
s
a
re
a
p
p
li
e
d
t
o
se
lec
t
o
p
ti
m
a
l
f
e
a
tu
re
s
to
a
d
d
re
ss
th
e
h
ig
h
d
ime
n
sio
n
a
li
ty
o
f
th
e
d
a
tas
e
t.
Th
is
wo
rk
p
r
o
p
o
s
e
s
a
n
e
w
h
y
b
rid
fe
a
t
u
re
se
lec
ti
o
n
m
e
th
o
d
,
n
a
m
e
ly
th
e
r
e
li
e
f
-
F
(R
F
)
-
g
e
n
e
t
ic
a
lg
o
rit
h
m
(G
A)
with
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
(S
VM)
c
las
sifica
ti
o
n
m
e
th
o
d
.
T
h
e
a
im
is
to
e
v
a
lu
a
te
t
h
e
p
e
rfo
rm
a
n
c
e
o
f
t
h
e
p
r
o
p
o
se
d
m
e
th
o
d
in
term
s
o
f
a
c
c
u
ra
c
y
,
c
o
m
p
u
tati
o
n
t
ime
,
a
n
d
t
h
e
n
u
m
b
e
r
o
f
se
lec
ted
fe
a
tu
re
s.
Th
e
m
e
th
o
d
is
imp
lem
e
n
ted
u
sin
g
P
y
th
o
n
in
P
y
Ch
a
rm
a
n
d
is
e
v
a
lu
a
t
e
d
o
n
a
DN
A
m
icro
a
rra
y
p
r
o
sta
te
c
a
n
c
e
r.
T
h
e
o
u
tco
m
e
o
f
t
h
is
w
o
rk
is
a
p
e
rfo
rm
a
n
c
e
c
o
m
p
a
riso
n
tab
le
fo
r
t
h
e
p
r
o
p
o
se
d
m
e
th
o
d
s
o
n
t
h
e
d
a
tas
e
t.
T
h
e
p
e
rfo
rm
a
n
c
e
o
f
G
A,
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
(
PSO
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,
a
n
d
w
h
a
le
o
p
t
imiz
a
ti
o
n
a
lg
o
rit
h
m
(
WOA
)
is
c
o
m
p
a
re
d
in
term
s
o
f
a
c
c
u
ra
c
y
,
c
o
m
p
u
tatio
n
ti
m
e
,
a
n
d
th
e
n
u
m
b
e
r
o
f
se
lec
ted
fe
a
tu
re
s
.
Re
su
lt
s
sh
o
w
th
a
t
G
A
h
a
s
th
e
h
ig
h
e
st
a
v
e
ra
g
e
a
c
c
u
ra
c
y
(9
1
.
1
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%
)
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o
m
p
a
re
d
to
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S
O
(9
0
.
5
2
%
)
a
n
d
WOA (8
5
.
7
4
%
)
.
G
A
o
u
tp
e
rfo
rm
s
P
S
O
a
n
d
WOA
d
u
e
to
it
s
su
p
e
rio
r
c
o
n
v
e
rg
e
n
c
e
p
ro
p
e
rti
e
s
a
n
d
b
e
tt
e
r
a
li
g
n
m
e
n
t
with
c
o
m
p
le
x
p
r
o
b
lem
s.
K
ey
w
o
r
d
s
:
Gen
etic
alg
o
r
ith
m
Par
ticle
s
war
m
o
p
tim
izatio
n
R
elief
-
F
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
W
h
ale
o
p
tim
izatio
n
alg
o
r
ith
m
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Nu
r
s
ab
illi
lah
Mo
h
d
Ali
Fak
u
lti T
ek
n
o
lo
g
i a
n
d
Keju
r
u
t
er
aa
n
E
lek
tr
ik
,
Un
iv
er
s
iti T
ek
n
ik
al
Ma
lay
s
ia
Me
lak
a
Me
lak
a,
Ma
lay
s
ia
E
m
ail:
n
u
r
s
ab
illi
lah
@
u
tem
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
C
an
ce
r
is
o
n
e
o
f
th
e
r
ea
s
o
n
s
o
f
m
o
r
tality
ar
o
u
n
d
th
e
wo
r
ld
,
esp
ec
ially
p
r
o
s
tate
ca
n
ce
r
[
1
]
.
I
t
o
cc
u
r
s
in
th
e
p
r
o
s
tate,
wh
ich
is
a
waln
u
t
-
s
h
ap
ed
g
lan
d
in
m
e
n
th
at
p
r
o
d
u
ce
s
s
em
in
al
f
lu
id
an
d
h
a
p
p
en
s
wh
en
th
er
e
is
u
n
co
n
tr
o
lled
g
r
o
wth
o
f
ab
n
o
r
m
al
ce
lls
in
th
e
p
r
o
s
tate
g
lan
d
.
L
ate
d
etec
tio
n
will
m
ak
e
th
e
s
itu
atio
n
wo
r
s
en
b
ec
au
s
e
ea
r
ly
s
tag
es
o
f
p
r
o
s
t
ate
ca
n
ce
r
o
f
ten
p
r
esen
t
with
m
in
im
al
o
r
n
o
s
y
m
p
to
m
s
.
No
wad
ay
s
,
d
o
cto
r
s
d
etec
t
ca
n
ce
r
b
y
co
n
d
u
ctin
g
X
-
R
ay
im
ag
in
g
,
lab
o
r
at
o
r
y
t
ests
(
in
clu
d
in
g
test
s
f
o
r
tu
m
o
r
m
ar
k
e
r
s
)
,
tu
m
o
r
b
io
p
s
y
,
en
d
o
s
co
p
ic
e
x
am
in
ati
o
n
,
a
n
d
s
u
r
g
er
y
.
T
h
ese
m
et
h
o
d
s
m
ay
lead
to
s
o
m
e
p
r
o
b
l
em
s
,
wh
ich
is
t
h
e
d
o
cto
r
s
m
a
y
h
av
e
m
is
s
ed
ca
n
ce
r
s
wh
en
r
ev
iewin
g
im
ag
in
g
s
ca
n
s
an
d
th
e
m
alf
u
n
ctio
n
o
f
t
h
e
test
in
g
m
ac
h
in
e
m
ay
o
cc
u
r
[
2
]
.
R
ec
en
tly
,
th
e
ad
v
an
ce
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
as
well
as
ar
tific
ial
in
tellig
en
ce
(
AI
)
h
av
e
r
esu
lted
in
d
ia
g
n
o
s
is
m
eth
o
d
s
f
o
r
ca
n
ce
r
[
3
]
.
T
h
u
s
,
th
e
r
ed
u
ctio
n
o
f
d
im
e
n
s
io
n
o
f
DNA
m
icr
o
ar
r
a
y
d
atasets
f
o
r
its
u
s
e
in
d
iag
n
o
s
i
s
is
cr
u
cial
to
d
etec
t p
r
o
s
tate
c
an
ce
r
at
an
ea
r
ly
s
tate
[
4
]
.
T
h
e
o
b
jectiv
e
o
f
th
e
s
tu
d
y
is
to
p
r
o
p
o
s
e
h
y
b
r
id
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
,
wh
ic
h
is
co
m
b
in
atio
n
o
f
r
elief
-
F
(
R
F)
f
ilter
with
g
en
etic
alg
o
r
ith
m
(
GA)
as
wr
ap
p
er
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
as
clas
s
if
ier
.
T
h
en
,
e
v
alu
ate
th
e
class
if
icatio
n
ac
cu
r
ac
y
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
o
n
p
r
o
s
tate
ca
n
ce
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyb
r
id
fea
tu
r
e
s
elec
tio
n
o
f m
icra
o
r
r
a
y
p
r
o
s
ta
te
ca
n
ce
r
…
(
N
u
r
s
a
b
illi
la
h
Mo
h
d
A
li
)
1885
DNA
m
icr
o
ar
r
ay
d
ataset
[
5
]
.
Nex
t,
co
m
p
a
r
e
th
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
e
n
t
o
p
tim
izer
s
in
t
er
m
s
o
f
ac
c
u
r
ac
y
,
co
m
p
u
tatio
n
tim
e,
an
d
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es f
r
o
m
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
f
ea
t
u
r
e
s
elec
tio
n
m
eth
o
d
.
T
h
e
f
ilter
m
eth
o
d
is
a
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
u
s
ed
in
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
to
r
ed
u
c
e
th
e
h
ig
h
d
im
en
s
io
n
ality
in
DNA
m
icr
o
ar
r
ay
d
ataset
[
6
]
−
[
8
]
.
T
h
e
R
elief
f
ilter
is
u
s
ed
ex
ten
s
iv
ely
t
o
id
en
tify
r
elev
an
t
f
ea
tu
r
es
f
o
r
b
u
ild
in
g
ac
cu
r
ate
d
iag
n
o
s
is
m
o
d
els
[
9
]
.
I
t
ev
alu
ates
th
e
q
u
ality
o
f
f
ea
tu
r
es
b
ased
o
n
h
o
w
well
th
eir
v
alu
es
d
is
tin
g
u
is
h
b
etwe
en
in
s
tan
ce
s
th
at
ar
e
n
ea
r
to
e
ac
h
o
th
e
r
.
E
s
s
en
tially
,
it
ass
ig
n
s
a
weig
h
t
f
o
r
ea
c
h
f
ea
tu
r
e
wh
ic
h
in
d
icate
s
th
e
a
b
ilit
y
o
f
th
at
f
ea
t
u
r
e
t
o
d
i
f
f
er
en
tiate
b
etwe
en
in
s
tan
ce
s
th
a
t
ar
e
clo
s
e
t
o
ea
ch
o
th
er
in
th
e
d
ataset
[
1
0
]
.
T
h
e
f
ea
tu
r
es with
th
e
h
ig
h
est we
ig
h
ts
ar
e
co
n
s
id
er
ed
th
e
m
o
s
t im
p
o
r
tan
t
.
T
h
e
wr
ap
p
er
m
eth
o
d
is
an
o
p
tim
izatio
n
tech
n
iq
u
e
u
s
ed
to
s
elec
t
th
e
o
p
tim
u
m
f
ea
tu
r
e
s
f
r
o
m
DNA
m
icr
o
ar
r
ay
d
ata
s
et
-
b
ased
o
n
m
etah
eu
r
is
tic
m
eth
o
d
[
1
1
]
−
[
1
3
]
.
Fo
r
ex
am
p
le,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
GA,
an
d
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
.
GA
is
ap
p
lied
b
y
th
e
p
r
o
ce
s
s
o
f
n
atu
r
al
s
elec
tio
n
wh
er
e
th
e
f
ittes
t
in
d
iv
id
u
als
ar
e
s
elec
ted
f
o
r
r
ep
r
o
d
u
ctio
n
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
o
f
th
e
n
ex
t
g
en
er
atio
n
.
Firstl
y
,
g
en
er
atin
g
a
n
in
itial
p
o
p
u
latio
n
r
an
d
o
m
ly
.
I
n
d
i
v
id
u
al
is
ch
ar
ac
ter
ized
b
y
a
s
et
o
f
p
ar
a
m
eter
s
(
v
ar
iab
les)
k
n
o
wn
as
g
en
es.
T
h
en
,
th
e
f
itn
ess
s
co
r
e
o
f
th
e
p
o
p
u
latio
n
is
ev
alu
ated
b
y
th
e
f
itn
ess
f
u
n
ctio
n
.
Nex
t,
in
d
iv
id
u
als
with
h
ig
h
er
f
itn
ess
s
co
r
es
ar
e
s
elec
ted
to
b
e
p
ar
en
ts
an
d
c
o
n
tr
ib
u
te
to
th
e
n
ex
t
g
en
e
r
atio
n
[
1
4
]
.
T
h
e
s
elec
ted
in
d
iv
id
u
al
s
ar
e
p
air
ed
to
p
er
f
o
r
m
cr
o
s
s
o
v
er
b
y
v
a
r
y
in
g
th
e
ch
r
o
m
o
s
o
m
es
(
s
o
lu
tio
n
)
to
cr
ea
te
n
ew
o
f
f
s
p
r
in
g
.
Mu
tatio
n
will
o
cc
u
r
wh
en
s
o
m
e
o
f
th
e
b
its
(
g
e
n
es)
in
th
e
s
tr
in
g
ca
n
f
lip
wh
ich
in
tr
o
d
u
ce
s
r
an
d
o
m
c
h
an
g
es
to
th
e
o
f
f
s
p
r
in
g
’
s
g
en
etic
in
f
o
r
m
atio
n
.
T
h
is
h
elp
s
m
ain
tain
a
n
d
im
p
r
o
v
e
g
e
n
etic
d
iv
er
s
ity
in
th
e
p
o
p
u
latio
n
.
T
h
e
o
ld
p
o
p
u
latio
n
will
b
e
r
ep
lace
d
b
y
th
e
n
ew
g
e
n
er
at
io
n
,
an
d
th
is
n
ew
p
o
p
u
latio
n
will b
e
u
s
ed
in
th
e
n
ex
t iter
atio
n
o
f
th
e
alg
o
r
ith
m
.
T
er
m
in
atio
n
o
cc
u
r
s
wh
en
a
s
atis
f
ac
to
r
y
s
o
lu
tio
n
is
f
o
u
n
d
,
o
r
th
e
alg
o
r
ith
m
co
n
v
er
g
es to
a
s
tab
le
s
tate.
C
las
s
if
icatio
n
is
a
ty
p
e
o
f
s
u
p
er
v
is
ed
lear
n
in
g
,
k
n
o
w
n
as
p
r
ed
ictin
g
th
e
class
o
f
a
g
iv
en
d
ata
p
o
in
t
b
elo
n
g
s
b
ased
o
n
s
p
ec
if
ied
class
if
icat
io
n
s
lab
el
.
SVM
is
ap
p
lied
to
s
o
lv
e
clas
s
if
icatio
n
is
s
u
es
[
1
5
]
.
T
h
e
f
u
n
d
am
e
n
tal
id
ea
b
e
h
in
d
SVM
is
to
f
in
d
th
e
b
est
b
o
u
n
d
ar
y
(
o
r
h
y
p
er
p
lan
e)
t
h
at
ca
n
s
ep
ar
ate
d
ata
class
es
with
th
e
m
ax
im
u
m
m
ar
g
in
p
o
s
s
ib
le.
Fo
r
lin
ea
r
ly
s
ep
ar
ab
le
ca
s
e,
SVM
ca
n
f
in
d
a
s
tr
aig
h
t
lin
e
(
in
2
D)
,
p
lan
e
(
in
3
D)
,
o
r
h
y
p
er
p
lan
e
(
in
h
i
g
h
er
d
im
en
s
io
n
s
)
th
at
s
ep
a
r
at
es
th
e
class
es
with
th
e
wid
est
p
o
s
s
ib
le
m
ar
g
in
.
T
h
er
e
ar
e
two
t
y
p
es o
f
SVM,
wh
ich
is
n
o
n
lin
ea
r
an
d
lin
ea
r
SVM
[
1
5
]
,
[
1
6
]
.
I
n
lin
ea
r
SVM,
t
h
e
d
ata
is
p
er
f
ec
tly
lin
ea
r
ly
s
ep
ar
ab
le,
w
h
ile
th
e
d
ata
i
n
n
o
n
lin
ea
r
S
VM
is
n
o
t
p
er
f
ec
tly
lin
ea
r
l
y
s
ep
ar
ab
le
.
Fo
r
n
o
n
lin
ea
r
ly
s
ep
ar
ab
le
ca
s
e
s
,
SVM
u
s
es
a
tech
n
iq
u
e
ca
ll
ed
th
e
k
er
n
el
tr
ic
k
.
A
k
er
n
el
is
a
f
u
n
ctio
n
th
at
m
ap
s
th
e
d
ata
to
a
h
ig
h
er
-
d
i
m
en
s
io
n
al
s
p
ac
e
wh
er
e
a
lin
ea
r
h
y
p
er
p
lan
e
ca
n
ef
f
ec
tiv
ely
p
er
f
o
r
m
t
h
e
s
ep
ar
a
tio
n
.
T
o
illu
s
tr
ate,
SVM
is
u
s
e
d
to
f
in
d
th
e
b
est
lin
e
th
at
cr
e
ates
th
e
wid
est
g
ap
b
etwe
en
th
e
two
g
r
o
u
p
s
.
B
ase
d
o
n
ex
is
tin
g
r
esear
ch
[
1
7
]
,
t
h
e
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
u
s
e
d
is
r
an
d
o
m
-
f
o
r
est
b
ased
f
ea
tu
r
e
s
elec
tio
n
(
RF
S)
with
SVM
as
class
if
ier
.
B
y
ap
p
ly
in
g
RF
S
+
SVM
f
ea
tu
r
e
s
e
lectio
n
m
eth
o
d
o
n
th
e
DNA
m
icr
o
ar
r
ay
p
r
o
s
tate
ca
n
ce
r
d
ataset,
th
e
b
est
p
r
ed
ictio
n
r
esu
lt
f
r
o
m
th
eir
wo
r
k
is
th
e
av
er
ag
e
ac
cu
r
ac
y
o
f
89
%
an
d
th
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es is
1
1
.
2.
M
E
T
H
O
D
I
n
th
e
m
eth
o
d
o
lo
g
y
th
e
r
e
ar
e
th
r
ee
s
tep
s
f
o
r
im
p
lem
en
tin
g
th
e
s
tu
d
y
.
T
h
e
f
ir
s
t
s
tep
in
v
o
lv
es
im
p
lem
en
tin
th
e
f
ilter
-
b
s
ae
d
m
eth
o
d
s
in
ex
p
er
im
en
t
1
.
Seco
n
d
ly
,
e
x
p
er
im
e
n
t
2
f
o
c
u
s
es
o
n
co
n
d
u
ctin
g
class
if
icatio
n
.
Fin
ally
,
ex
p
er
i
m
en
t 3
in
v
o
lv
es o
p
tim
izatio
n
an
d
co
m
p
ar
is
o
n
o
f
p
e
r
f
o
r
m
an
ce
.
2
.
1
.
E
x
perim
ent
1
:
perf
o
r
m
s
ing
le
RF
f
ilte
r
m
et
ho
d o
n pro
s
t
a
t
e
ca
ncer
da
t
a
s
et
Fig
u
r
e
1
s
h
o
ws
R
F
Fil
ter
m
e
th
o
d
.
T
h
e
s
tep
-
by
-
s
tep
m
et
h
o
d
is
b
r
ief
ly
d
icu
s
s
ed
in
th
e
f
o
llo
win
g
:
t
o
an
aly
ze
a
p
r
o
s
tate
ca
n
ce
r
DNA
m
icr
o
ar
r
ay
d
ataset,
ess
en
tial
lib
r
ar
ies
s
u
ch
as
Pan
d
a
s
,
Nu
m
Py
,
an
d
RF
wer
e
im
p
o
r
ted
in
Py
C
h
ar
m
.
T
h
e
d
ataset
was
lo
ad
ed
f
r
o
m
a
co
m
m
a
s
ep
ar
ated
v
alu
es
(
C
SV
)
f
ile,
an
d
f
ea
tu
r
e
v
ar
iab
les
wer
e
e
x
tr
ac
ted
b
y
d
r
o
p
p
in
g
th
e
last
co
lu
m
n
,
wh
ich
co
n
tain
ed
t
h
e
lab
els,
a
n
d
s
to
r
in
g
th
em
in
f
ea
tu
r
e_
x
.
L
a
b
e
l
s
w
e
r
e
e
x
t
r
a
cte
d
,
c
o
n
v
e
r
t
e
d
t
o
n
u
m
e
r
i
c
a
l
v
a
lu
e
s
u
s
i
n
g
a
L
a
b
e
l
E
n
c
o
d
e
r
,
a
n
d
s
t
o
r
e
d
i
n
t
a
r
g
e
t
_
y
.
T
h
e
d
a
t
a
w
a
s
t
h
e
n
s
p
l
i
t
i
n
t
o
t
r
a
i
n
i
n
g
a
n
d
t
e
s
t
i
n
g
s
e
ts
w
i
t
h
a
0
.
2
r
a
t
i
o
.
T
h
e
RF
m
e
t
h
o
d
w
as
a
p
p
l
i
e
d
t
o
s
e
l
ec
t
t
h
e
1
0
m
o
s
t
r
e
l
e
v
a
n
t
f
e
at
u
r
e
s
,
a
n
d
t
h
e
f
i
l
t
e
r
e
d
d
at
a
s
e
t
’
s
d
i
m
e
n
s
io
n
s
w
e
r
e
c
h
e
c
k
e
d
t
o
c
o
n
f
i
r
m
a
c
c
u
r
a
c
y
.
T
h
is
f
ir
s
t
s
tep
o
f
FS
is
cr
u
cial
a
s
it
wi
ll
f
ilter
o
u
t
u
n
r
elev
an
t
f
ea
t
u
r
es,
u
p
d
ate
wig
h
ts
an
d
r
an
k
f
ea
tu
r
es
with
h
ig
h
er
weu
g
h
ts
.
Featu
r
es with
h
ig
h
er
weig
h
ts
ar
e
co
n
s
id
er
e
d
m
o
r
e
r
elev
an
t f
o
r
th
e
class
if
icatio
n
task
.
2
.
2
.
E
x
perim
ent
2
:
ev
a
lua
t
io
n o
n RF
f
ilte
r
’
s
cla
s
s
if
ica
t
io
n a
cc
ura
cy
by
us
ing
SVM
cla
s
s
if
ier
Fig
u
r
e
2
s
h
o
ws
th
e
clas
s
if
ca
ito
n
af
ter
p
er
f
o
r
m
in
g
th
e
R
F
f
ilter
m
eth
o
d
.
T
h
e
n
ex
t
ex
p
er
im
en
t
is
d
icu
s
s
io
n
ab
o
u
t
class
if
icatio
n
u
s
in
g
SVM.
W
ith
th
e
S
VM
m
ac
h
in
e
lear
n
in
g
m
o
d
el
,
th
e
lib
r
ar
ies
in
Py
C
h
ar
m
wer
e
s
et
.
T
h
e
SVM
class
if
ie
r
was
s
elec
ted
an
d
f
itted
wit
h
th
e
f
ilter
ed
tr
ain
in
g
s
et.
T
r
ain
in
g
an
d
test
in
g
ac
cu
r
ac
y
wer
e
e
v
alu
ated
u
s
in
g
th
e
class
if
ier
’
s
p
r
ed
ictio
n
f
u
n
ctio
n
.
Step
s
wer
e
r
ep
ea
ted
w
ith
v
ar
y
in
g
n
u
m
b
er
s
o
f
s
elec
ted
f
ea
tu
r
es
(
5
0
,
1
0
0
,
2
0
0
,
5
0
0
,
1
0
0
0
,
2
0
0
0
)
to
a
n
aly
ze
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
n
u
m
b
er
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
8
4
-
1
8
9
4
1886
f
ea
tu
r
es
an
d
ac
cu
r
ac
y
.
Ne
x
t,
t
h
e
o
p
tim
izer
s
we
r
e
ap
p
lied
a
n
d
co
m
p
ar
e
d
ag
ai
n
s
t
ea
ch
o
th
er
u
s
in
g
PS
O,
GA,
an
d
W
OA.
Fig
u
r
e
1
.
Fil
ter
m
eth
o
d
Fig
u
r
e
2
.
C
lass
if
icatio
n
with
SVM
2
.
3
.
E
x
perim
ent
3
:
o
pti
m
iza
t
io
n o
n
RF
f
ilte
r
by
us
ing
wra
pp
er
(
G
A,
P
SO
,
o
r
WO
A)
2
.
3
.
1
.
Wra
pp
er
ba
s
ed
G
A
Fig
u
r
e
3
s
h
o
ws
th
e
f
lo
wch
ar
t
o
f
GA
wr
ap
p
er
-
b
ased
m
eth
o
d
.
T
h
e
p
ar
a
m
eter
s
o
f
th
e
GA
wr
ap
p
er
wer
e
s
et,
in
clu
d
in
g
a
p
o
p
u
latio
n
s
ize
e
q
u
al
t
o
th
e
n
u
m
b
e
r
o
f
s
am
p
les
in
th
e
d
ataset
(
1
0
2
)
,
a
m
a
x
im
u
m
o
f
5
0
g
en
er
atio
n
s
,
a
n
d
a
m
u
tatio
n
r
ate
o
f
0
.
1
.
T
h
e
GA
was
co
n
d
u
cted
.
T
h
e
SVM
class
if
ier
was
s
elec
ted
an
d
f
itted
with
ea
ch
n
ew
g
en
er
atio
n
[
1
8
]
,
[
1
9
]
.
T
h
e
f
itn
ess
s
co
r
e
o
f
ea
ch
g
en
er
atio
n
was
ev
alu
ated
u
s
in
g
th
e
test
in
g
s
et
an
d
th
e
class
if
ier
’
s
p
r
ed
ictio
n
f
u
n
ctio
n
.
Step
s
wer
e
r
ep
ea
te
d
u
n
til
th
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
g
en
er
atio
n
s
was
r
ea
ch
ed
,
a
n
d
th
e
f
itn
ess
s
co
r
e
(
ac
cu
r
ac
y
)
o
f
ea
c
h
g
e
n
er
atio
n
was
g
en
er
ated
.
2
.
3
.
2
.
Wra
pp
er
ba
s
ed
P
SO
Fig
u
r
e
4
s
h
o
ws
t
h
e
f
l
o
wch
ar
t
o
f
PSO
wr
ap
p
er
-
b
ased
m
eth
o
d
,
th
e
s
tep
-
by
-
s
tep
m
eth
o
d
is
d
is
cu
s
s
ed
as
f
o
llo
ws
.
T
h
e
n
ec
ess
ar
y
lib
r
ar
y
f
o
r
PS
O
is
s
et
.
T
h
e
p
ar
a
m
eter
s
f
o
r
th
e
PS
O
wr
ap
p
er
a
r
e
s
et
,
in
clu
d
in
g
1
0
p
ar
ticles
an
d
5
0
iter
atio
n
s
.
PS
O
is
th
en
co
n
d
u
cted
,
an
d
th
e
p
ar
ticle
p
o
s
itio
n
s
ar
e
f
i
tted
in
to
th
e
SVM
class
if
ier
.
T
h
e
f
itn
ess
s
co
r
e
o
f
ea
ch
p
ar
ticle
’
s
p
o
s
itio
n
is
e
v
alu
ated
u
s
in
g
th
e
test
in
g
s
et
in
th
e
clas
s
if
ier
’
s
p
r
ed
ictio
n
f
u
n
ctio
n
.
Af
ter
ev
e
r
y
iter
atio
n
,
th
e
p
o
s
itio
n
an
d
v
e
lo
city
o
f
ea
ch
p
ar
ticle
ar
e
u
p
d
ated
.
T
h
e
s
tep
s
ar
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyb
r
id
fea
tu
r
e
s
elec
tio
n
o
f m
icra
o
r
r
a
y
p
r
o
s
ta
te
ca
n
ce
r
…
(
N
u
r
s
a
b
illi
la
h
Mo
h
d
A
li
)
1887
r
ep
ea
ted
u
n
til
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
is
r
ea
ch
ed
.
Fin
ally
,
th
e
f
itn
ess
s
co
r
e
(
ac
cu
r
ac
y
)
is
g
en
er
at
ed
o
u
t.
T
h
e
n
ex
t step
in
v
o
lv
es a
p
p
ly
in
g
f
o
r
W
OA.
Fig
u
r
e
3
.
T
h
e
f
lo
wch
a
r
t
o
f
GA
Fig
u
r
e
4
.
PS
O
wr
ap
p
e
r
-
b
ased
f
lo
wch
ar
t
2
.
3
.
3
.
Wra
pp
er
ba
s
ed
WO
A
Fig
u
r
e
5
s
h
o
ws
th
e
f
lo
wch
ar
t
o
f
W
OA
wr
ap
p
er
-
b
ased
m
eth
o
d
.
T
h
e
n
u
m
b
er
o
f
f
ea
tu
r
es
s
elec
ted
b
y
th
e
RF
f
ilter
is
s
e
t
as
1
0
.
T
h
e
p
ar
am
eter
s
f
o
r
W
OA
ar
e
s
et:
1
0
wh
ales
an
d
5
0
iter
atio
n
s
.
W
OA
i
s
co
n
d
u
cted
,
an
d
th
e
wh
ale
p
o
s
itio
n
s
ar
e
f
itted
in
to
th
e
SVM
cla
s
s
if
ie
r
.
T
h
e
f
itn
ess
s
co
r
e
o
f
ea
ch
wh
ale
’
s
p
o
s
itio
n
i
s
ev
alu
ated
u
s
in
g
th
e
test
in
g
s
e
t
in
th
e
class
if
ier
’
s
p
r
ed
ictio
n
f
u
n
ctio
n
.
T
h
e
p
o
s
itio
n
o
f
ea
c
h
wh
ale
is
u
p
d
ated
af
ter
ev
er
y
iter
atio
n
.
Step
s
ar
e
r
ep
ea
ted
u
n
til
th
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
is
r
e
ac
h
ed
.
Fin
ally
,
th
e
f
itn
ess
s
co
r
e
(
ac
cu
r
ac
y
)
u
s
in
g
in
(
1
)
is
p
r
o
d
u
ce
d
.
T
h
e
n
ex
t
p
r
o
ce
s
s
is
co
m
p
ar
in
g
th
e
th
r
ee
o
p
tim
izer
s
an
d
id
en
tifie
d
th
e
b
est r
esu
lts
f
r
o
m
th
e
th
r
ee
o
p
tim
izer
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
8
4
-
1
8
9
4
1888
Fig
u
r
e
5
.
W
OA
wr
ap
p
er
-
b
ase
d
m
eth
o
d
f
lo
wch
a
r
t
2
.
4
.
P
er
f
o
r
m
a
nce
co
m
pa
r
is
o
n bet
wee
n G
A,
P
SO
,
a
nd
W
O
A
T
h
e
last
s
tep
is
co
n
d
u
cted
th
e
co
m
p
a
r
is
o
n
b
etwe
en
GA,
PS
O
,
an
d
W
OA
o
p
tim
izer
.
Fig
u
r
e
6
s
h
o
ws
th
e
p
r
o
ce
s
s
.
A
co
m
p
ar
is
o
n
tab
le
o
f
GA,
PS
O,
an
d
W
OA
i
s
m
ad
e
in
ter
m
s
o
f
ac
cu
r
ac
y
,
co
m
p
u
tatio
n
tim
e,
an
d
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es.
T
h
e
p
er
f
o
r
m
an
ce
o
f
GA,
PS
O,
an
d
W
OA
i
s
v
is
u
alize
d
w
ith
g
r
ap
h
s
s
h
o
win
g
ac
cu
r
ac
y
,
co
m
p
u
tatio
n
tim
e,
a
n
d
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
e
s
.
T
h
e
b
est
-
p
er
f
o
r
m
in
g
o
p
tim
i
ze
r
is
s
e
lecte
d
f
o
r
m
ain
u
s
e
b
ased
o
n
th
e
m
etr
ics
s
u
ch
as a
cc
u
r
ac
y
,
c
o
m
p
u
tatio
n
tim
e
an
d
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es.
Fig
u
r
e
6
.
Su
m
m
ar
y
wo
r
k
b
etwe
en
GA,
PS
O
,
an
d
W
OA
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyb
r
id
fea
tu
r
e
s
elec
tio
n
o
f m
icra
o
r
r
a
y
p
r
o
s
ta
te
ca
n
ce
r
…
(
N
u
r
s
a
b
illi
la
h
Mo
h
d
A
li
)
1889
Fin
ally
,
th
e
class
if
icatio
n
ac
cu
r
ac
y
’
s
[
2
0
]
u
s
ed
f
o
r
th
e
in
d
icatin
g
th
e
p
er
f
o
r
m
an
ce
ev
alu
atio
n
an
d
ca
n
b
e
co
m
p
u
ted
as
(
1
)
:
Acc
u
r
ac
y
=
+
+
+
+
(
1
)
i
n
(
1
)
s
h
o
ws
th
e
ac
cu
r
ac
y
co
m
p
u
tatio
n
.
T
r
u
e
p
o
s
itiv
e
(
T
P)
r
ep
r
esen
ts
co
r
r
ec
tly
class
if
ie
d
p
o
s
itiv
e
s
am
p
les.
T
r
u
e
n
eg
ativ
e
(
T
N)
d
en
o
tes
co
r
r
ec
tly
class
if
ied
n
eg
ativ
e
s
am
p
les.
Fals
e
p
o
s
itiv
e
(
F
P)
r
ef
er
s
to
n
eg
ativ
e
s
am
p
les
th
at
wer
e
m
is
class
if
i
ed
as
p
o
s
itiv
e,
wh
ile
f
alse
n
e
g
ativ
e
(
FN)
in
d
icate
s
p
o
s
itiv
e
s
am
p
les
th
at
wer
e
m
is
class
if
ied
as n
eg
ativ
e.
3.
DIS
CU
SS
I
O
N
T
h
is
s
ec
tio
n
d
is
cu
s
s
ed
th
e
r
esu
lt
an
d
d
is
cu
s
s
io
n
o
f
th
e
e
x
p
e
r
im
en
t.
T
h
e
r
e
ar
e
f
iv
e
ex
p
er
i
m
en
ts
h
av
e
b
ee
n
co
n
d
u
cted
n
am
ely
p
er
f
o
r
m
in
g
f
ilter
m
eth
o
d
u
s
in
g
R
elief
m
eth
o
d
,
class
if
icatio
n
,
wr
ap
p
er
m
eth
o
d
,
o
b
s
er
v
ed
th
e
p
er
f
o
r
m
a
n
ce
an
d
th
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es.
3
.
1
.
E
x
perim
ent
1
:
perf
o
r
m
s
ing
le
RF
f
ilte
r
m
et
ho
d o
n pro
s
t
a
t
e
ca
ncer
da
t
a
s
et
I
n
ex
p
er
im
en
t
1
,
t
h
e
DNA
m
i
cr
o
ar
r
ay
d
ataset
u
s
ed
is
th
e
D
NA
m
icr
o
ar
r
ay
p
r
o
s
tate
ca
n
ce
r
d
ataset.
B
ased
o
n
T
ab
le
1
,
t
h
e
d
im
en
s
io
n
o
f
th
e
p
r
o
s
tate
ca
n
ce
r
d
at
aset
b
ef
o
r
e
f
ilter
ed
is
(
1
0
2
,
1
2
6
0
0
)
,
wh
ich
m
ea
n
s
th
at
th
e
d
ataset
h
as 1
0
2
r
o
ws a
n
d
1
2
6
0
0
c
o
lu
m
n
s
r
ep
r
esen
ts
p
r
o
s
tate
g
en
es
[
5
]
.
T
h
is
ca
n
b
e
p
r
o
v
e
n
b
y
lo
a
d
in
g
th
e
csv
f
ile
of
p
r
o
s
tate
ca
n
ce
r
d
ataset
n
am
ed
‘
p
r
o
s
tate_
ca
n
c
er
_
DNA_
m
icr
o
ar
r
a
y
.
csv
’
in
P
y
C
h
ar
m
an
d
u
s
in
g
p
an
d
as
lib
r
a
r
y
t
o
r
ea
d
th
e
csv
f
ile.
T
h
is
m
ea
n
s
th
at
th
e
d
ata
s
et
is
s
ep
ar
ated
in
to
f
ea
tu
r
es
an
d
lab
el
v
ar
ia
b
le
,
wh
er
e
th
e
lab
el
v
ar
iab
le
is
at
th
e
last
co
lu
m
n
o
f
th
e
d
ataset,
an
d
t
h
e
f
ea
tu
r
es
v
ar
iab
le
is
t
h
e
co
lu
m
n
s
ex
ce
p
t
th
e
last
co
lu
m
n
.
3
.
2
.
Ca
lcula
t
i
o
n
o
f
t
he
t
r
a
ini
ng
s
et
a
nd
t
esting
s
et
ra
t
io
o
f
8
0
/2
0
T
h
e
R
F
f
ilter
s
tar
ted
b
y
in
itia
lizin
g
th
e
weig
h
ts
o
f
all
f
ea
tu
r
es
in
p
r
o
s
tate
ca
n
ce
r
DNA
m
icr
o
ar
r
ay
d
ataset
to
ze
r
o
.
T
h
en
,
a
s
am
p
le
in
s
tan
ce
is
s
elec
ted
f
r
o
m
th
e
d
ataset
r
an
d
o
m
l
y
.
T
h
is
in
s
tan
ce
is
r
ef
er
r
ed
as
th
e
tar
g
et
in
s
tan
ce
.
Af
ter
th
at,
RF
f
ilter
will
f
in
d
th
e
n
ea
r
est
h
its
an
d
n
ea
r
est
m
is
s
es
b
ased
o
n
th
e
tar
g
et
in
s
tan
ce
.
Nea
r
est
h
it
is
th
e
clo
s
est
in
s
tan
ce
to
th
e
tar
g
et
i
n
s
tan
ce
th
at
h
as
th
e
s
am
e
class
lab
el,
wh
il
e
n
ea
r
est
m
is
s
is
th
e
clo
s
est in
s
tan
ce
to
th
e
tar
g
et
in
s
tan
ce
b
u
t w
ith
a
d
if
f
er
en
t c
l
ass
lab
el.
T
h
en
,
ea
ch
f
ea
t
u
r
e
will u
p
d
ate
its
weig
h
t
b
ased
o
n
h
o
w
well
it d
is
tin
g
u
i
s
h
es th
e
tar
g
et
in
s
tan
ce
f
r
o
m
t
h
e
n
ea
r
est h
it a
n
d
th
e
n
ea
r
est m
is
s
.
B
ased
o
n
th
e
RF
u
p
d
ate
r
u
le
ab
o
v
e,
th
e
weig
h
t
is
d
ec
r
ea
s
ed
if
th
e
d
if
f
e
r
en
ce
b
etwe
en
th
e
tar
g
et
in
s
tan
ce
an
d
its
n
ea
r
est
h
it
a
r
e
lar
g
er
(
im
p
ly
in
g
th
is
f
ea
tu
r
e
is
less
im
p
o
r
tan
t
f
o
r
class
s
im
ilar
ity
)
.
I
n
co
n
tr
ast,
th
e
weig
h
t
is
in
cr
e
ased
if
t
h
e
d
if
f
er
en
ce
b
etwe
en
t
h
e
tar
g
et
in
s
tan
ce
an
d
its
n
e
ar
est
m
is
s
ar
e
lar
g
er
(
im
p
l
y
in
g
th
is
f
ea
tu
r
e
is
im
p
o
r
tan
t
f
o
r
d
is
tin
g
u
is
h
in
g
b
etwe
en
d
i
f
f
er
en
t
class
es).
Af
ter
iter
atin
g
th
r
o
u
g
h
th
e
d
ataset,
th
e
to
p
1
0
f
ea
tu
r
es
with
th
e
h
ig
h
est
weig
h
ts
ar
e
co
n
s
id
er
ed
th
e
m
o
s
t
im
p
o
r
tan
t
an
d
will
b
e
u
s
ed
f
o
r
m
ac
h
in
e
tr
ain
in
g
m
o
d
el.
I
n
o
th
er
wo
r
d
s
,
th
is
i
s
d
o
n
e
b
y
s
elec
tin
g
th
e
f
ir
s
t
1
0
o
f
th
e
f
ea
tu
r
es
b
ased
o
n
th
eir
en
tr
o
p
y
m
ea
s
u
r
e
r
an
k
in
g
i
n
d
escen
d
i
n
g
o
r
d
er
.
T
h
e
en
tr
o
p
y
m
ea
s
u
r
e
r
ep
r
esen
ts
r
an
d
o
m
n
ess
in
a
f
ea
tu
r
e,
0
m
ea
n
s
th
e
m
o
s
t,
an
d
1
m
ea
n
s
least,
th
u
s
th
e
h
ig
h
e
r
th
e
en
tr
o
p
y
m
ea
s
u
r
e
s
,
th
e
m
o
r
e
u
s
ef
u
l th
e
f
ea
tu
r
e
is
.
T
h
e
ca
lcu
latio
n
s
ca
n
b
e
ea
s
ily
m
ad
e
t
o
p
r
o
v
e
th
e
r
atio
o
f
t
h
e
tr
ain
in
g
s
et
to
th
e
test
in
g
s
et
will b
e
8
0
/2
0
.
T
esti
n
g
s
et
s
am
p
les =
(
s
p
lit s
i
ze
)
(
n
o
.
s
am
p
les)
=
(
0
.
2
)
(
1
0
2
)
=
2
0
.
4
=
2
1
T
r
ain
in
g
s
et
s
am
p
les =
(
n
o
.
s
am
p
les)
–
(
n
o
.
test
in
g
s
am
p
les)
=
1
0
2
–
2
1
=
8
1
3
.
3
.
E
x
perim
ent
2
:
ev
a
lua
t
io
n
o
n RF
F
ilte
r
’
s
cla
s
s
if
ica
t
io
n a
cc
ura
cy
by
us
ing
SVM
cl
a
s
s
if
ier
I
n
E
x
p
er
im
en
t
2
,
th
e
ev
al
u
atio
n
o
f
R
F
f
ilter
’
s
class
i
f
icatio
n
ac
cu
r
ac
y
is
m
ad
e
b
y
u
s
in
g
SVM
class
if
ier
.
Af
ter
R
F
f
i
lter
s
th
e
f
ea
tu
r
es
in
p
r
o
s
tat
e
ca
n
ce
r
DNA
m
icr
o
ar
r
a
y
d
ataset,
SVM
will
ca
lcu
late
th
e
tr
ain
in
g
an
d
test
in
g
ac
cu
r
ac
y
.
T
h
e
n
u
m
b
er
o
f
th
e
tr
ain
in
g
f
ea
tu
r
e
is
r
an
g
es
f
r
o
m
2
5
,
5
0
,
1
0
0
,
2
0
0
,
5
0
0
.
B
ased
o
n
Fig
u
r
e
7
,
wh
en
th
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es
in
cr
ea
s
e,
th
e
tr
ain
in
g
an
d
test
in
g
ac
cu
r
ac
y
will
d
ec
r
ea
s
e.
I
t
is
b
ec
au
s
e
t
h
e
R
F
f
ilter
d
o
es
n
o
t
f
ilter
o
u
t
th
e
less
u
s
ef
u
l
f
ea
tu
r
es
i
n
d
ataset
an
d
t
h
is
will
d
ec
r
ea
s
e
th
e
e
f
f
icien
cy
o
f
th
e
r
esu
lts
.
T
h
u
s
,
h
ig
h
er
th
e
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es,
h
i
g
h
er
t
h
e
p
o
s
s
ib
ilit
y
o
f
less
u
s
ef
u
l
f
ea
t
u
r
es
n
o
t
f
il
ter
ed
o
u
t,
an
d
m
a
y
lo
wer
th
e
ac
cu
r
ac
y
.
T
o
o
v
e
r
co
m
e
th
i
s
is
s
u
e,
th
e
h
y
b
r
id
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
is
p
r
o
p
o
s
ed
t
o
m
a
k
e
o
p
tim
izatio
n
to
f
u
r
t
h
er
s
elec
t
th
e
o
p
tim
u
m
f
ea
tu
r
es
b
y
u
s
in
g
a
wr
ap
p
er
m
eth
o
d
lik
e
GA,
P
SO,
an
d
W
OA,
wh
ich
is
m
o
r
e
ac
cu
r
ate
d
u
e
to
its
ef
f
i
cien
t
o
p
tim
izatio
n
alg
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
8
4
-
1
8
9
4
1890
Fig
u
r
e
7.
T
r
ain
in
g
a
n
d
test
in
g
ac
cu
r
ac
y
ag
ai
n
s
t n
o
.
s
elec
ted
f
ea
tu
r
e
3
.
4
.
E
x
perim
ent
3:
o
pti
m
iza
t
io
n o
n
RF
f
ilte
r
by
us
ing
diff
er
ent
wra
pp
er
(
G
A,
P
SO
,
o
r
WO
A)
R
ec
en
t
y
ea
r
s
h
av
e
s
ee
n
ex
ten
s
iv
e
ex
p
lo
r
atio
n
o
f
v
ar
io
u
s
tech
n
iq
u
e
an
d
m
eth
o
d
f
o
r
a
n
aly
zin
g
an
d
class
if
y
in
g
p
r
o
s
tate
ca
n
ce
r
.
T
h
e
co
m
p
ar
ativ
e
a
n
aly
s
is
in
T
ab
le
1
s
u
g
g
ests
a
s
u
p
er
io
r
m
eth
o
d
to
p
r
e
v
io
u
s
s
tu
d
ies
o
n
th
e
s
am
e
d
ataset.
T
h
e
f
in
d
i
n
g
in
d
icate
th
at
t
h
e
R
elif
e
-
GA+
SVM
h
y
b
r
id
ap
p
r
o
ac
h
p
er
f
o
r
m
s
p
r
o
m
is
in
g
ly
.
B
ased
o
n
T
ab
le
1
,
th
e
p
er
f
o
r
m
an
ce
b
etwe
en
GA,
PS
O,
W
OA
is
co
m
p
ar
e
d
to
ea
ch
o
th
er
in
ter
m
s
o
f
ac
cu
r
ac
y
,
co
m
p
u
tatio
n
tim
e,
an
d
n
u
m
b
er
o
f
s
elec
ted
f
ea
tu
r
es.
PS
O
h
a
s
th
e
f
aste
s
t
co
m
p
u
tatio
n
tim
e
[
2
1
]
(
4
.
6
6
s
)
if
c
o
m
p
ar
e
d
to
GA
(
1
7
.
6
3
s
)
a
n
d
W
OA
(
4
.
7
4
s
)
as
in
T
ab
le
1
.
PS
O
an
d
W
OA
h
a
v
e
alm
o
s
t
th
e
s
am
e
s
p
ee
d
,
wh
ich
is
ab
o
u
t
f
o
u
r
s
ec
o
n
d
s
,
wh
en
h
a
n
d
lin
g
th
e
o
p
tim
izatio
n
o
f
p
r
o
s
tate
ca
n
ce
r
DNA
m
icr
o
ar
r
ay
d
ataset.
W
h
ile
GA
n
ee
d
s
th
e
lo
n
g
est co
m
p
u
tatio
n
tim
e,
wh
i
ch
is
1
7
.
6
3
s
,
to
h
an
d
le
th
e
o
p
ti
m
izatio
n
o
f
d
ataset.
Fig
u
r
e
8
s
h
o
ws
th
at
GA
-
SVM
h
as
th
e
h
ig
h
est
av
er
ag
e
ac
cu
r
ac
y
(
9
1
.
1
7
%)
if
co
m
p
ar
ed
to
PS
O(
9
0
.
5
2
%)
an
d
W
OA
(
8
5
.
7
4
%).
GA
o
u
tp
e
r
f
o
r
m
s
PS
O
an
d
W
OA
d
u
e
to
its
s
u
p
er
io
r
co
n
v
er
g
en
ce
p
r
o
p
er
ties
a
n
d
b
etter
alig
n
m
e
n
t
with
co
m
p
lex
p
r
o
b
lem
la
n
d
s
ca
p
es.
I
ts
ab
ilit
y
t
o
b
alan
ce
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
ef
f
ec
tiv
ely
h
el
p
s
av
o
id
lo
ca
l
o
p
tim
a,
m
a
k
in
g
it
p
ar
ticu
lar
ly
s
u
itab
le
f
o
r
ch
alle
n
g
in
g
o
p
tim
izatio
n
d
o
m
ain
s
.
p
r
i
n
cip
al
co
m
p
o
n
e
n
t
an
aly
s
is
(
PC
A
)
an
d
k
er
n
el
-
b
ased
p
r
in
cip
al
co
m
p
o
n
e
n
t
an
a
ly
s
is
(
KP
C
A
)
wer
e
u
tili
ze
d
b
y
Xiao
x
u
e
et
al
.
[
2
2
]
to
s
im
p
lify
a
n
d
id
e
n
tify
p
r
o
s
tate
ca
n
ce
r
.
I
n
th
eir
f
in
d
in
g
s
,
KPC
A
o
u
tp
er
f
o
r
m
e
d
PC
A
with
SVM
m
o
d
els
b
ased
o
n
cu
m
u
lativ
e
co
n
tr
ib
u
tio
n
.
Desp
ite
KPC
A
b
ein
g
m
o
r
e
ef
f
icien
t,
PC
A
was
m
o
r
e
e
f
f
ec
tiv
e
i
n
d
im
en
s
io
n
ality
r
ed
u
ctio
n
,
alth
o
u
g
h
it
h
a
d
a
h
ig
h
r
u
n
n
in
g
tim
e
o
f
1
8
.
3
1
s
ec
o
n
d
s
an
d
t
h
e
ac
cu
r
ac
y
o
f
8
4
.
3
1
%
was
ac
h
iev
ed
.
Hig
h
lig
h
tin
g
t
h
eir
s
ig
n
if
ican
t
co
n
tr
ib
u
tio
n
.
No
tab
le
lim
itatio
n
s
in
clu
d
e
d
ataset
d
im
en
s
io
n
,
p
o
ten
tio
al
ch
allen
g
es
in
clin
ical
im
p
lem
n
tatio
n
an
d
th
e
p
r
o
b
lem
in
th
e
s
im
p
lific
atio
n
o
f
th
e
f
ea
tu
r
e
r
e
d
u
ctio
n
p
r
o
ce
s
s
.
Ho
wev
er
,
th
e
n
u
m
b
e
r
o
f
s
elec
ted
f
ea
tu
r
es
was
n
o
t
r
ep
o
r
ted
in
th
eir
f
i
n
d
in
g
s
.
E
x
p
er
im
en
tal
r
esu
lts
in
d
icate
th
at
th
e
p
r
o
p
o
s
ed
R
elief
-
GA+
SVM
h
y
b
r
id
ap
p
r
o
ac
h
ac
h
iev
ed
t
h
e
h
ig
h
est
ac
cu
r
ac
y
o
f
9
1
.
1
7
%
s
u
r
p
ass
in
g
p
r
ev
io
u
s
m
eth
o
d
s
s
u
ch
as th
o
s
e
b
y
Xiao
x
u
e
et
a
l
.
[
2
2
]
.
Fo
r
GA,
it
wo
r
k
s
th
r
o
u
g
h
m
ec
h
an
is
m
s
,
s
u
ch
as
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
.
E
ac
h
o
f
th
ese
s
tep
s
in
v
o
lv
es
s
ig
n
if
ican
t
co
m
p
u
ta
tio
n
tim
e.
C
r
o
s
s
o
v
er
in
v
o
lv
es
p
air
in
g
s
elec
ted
in
d
iv
id
u
als
(
s
o
lu
tio
n
)
an
d
ex
ch
an
g
i
n
g
p
o
r
tio
n
s
o
f
th
ei
r
g
en
etic
in
f
o
r
m
atio
n
to
cr
ea
te
n
ew
o
f
f
s
p
r
in
g
.
T
h
e
in
d
i
v
id
u
al
s
elec
ted
is
b
ased
o
n
th
eir
f
itn
ess
s
co
r
es
an
d
th
is
u
s
u
ally
r
eq
u
ir
es
s
o
r
tin
g
o
r
r
a
n
k
i
n
g
o
p
er
atio
n
s
,
wh
ich
a
r
e
c
o
m
p
u
tatio
n
ally
c
o
s
tly
.
Af
ter
cr
o
s
s
o
v
er
,
m
u
tatio
n
alter
s
th
e
g
e
n
es
o
f
th
e
n
ew
g
en
er
atio
n
r
a
n
d
o
m
l
y
,
a
n
d
it
r
eq
u
ir
es
ad
d
itio
n
al
co
m
p
u
tatio
n
f
o
r
ea
ch
g
e
n
e
m
u
tatio
n
.
T
h
u
s
,
GA
ten
d
s
to
ex
p
lo
r
e
th
e
s
o
lu
tio
n
s
p
ac
e
m
o
r
e
th
o
r
o
u
g
h
ly
d
u
e
to
its
d
iv
e
r
s
e
o
p
er
atio
n
s
,
s
o
th
at
s
lo
wer
th
e
c
o
n
v
e
r
g
en
ce
.
Fo
r
PS
O
an
d
W
OA,
th
ey
ca
n
co
n
v
er
g
e
f
aster
to
a
s
o
lu
tio
n
b
ec
a
u
s
e
o
f
th
eir
s
im
p
ler
u
p
d
ate
r
u
les,
wh
ich
is
d
ir
ec
tly
in
teg
r
ate
th
e
b
est
-
k
n
o
wn
p
o
s
i
tio
n
s
(
s
o
lu
tio
n
)
with
o
u
t
th
e
d
is
r
u
p
tiv
e
s
tep
s
lik
e
m
u
tatio
n
in
GA.
Alth
o
u
g
h
th
e
PS
O
an
d
W
OA
co
m
p
u
tatio
n
s
p
ee
d
is
f
ast,
lack
o
f
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
s
tep
s
m
ig
h
t in
f
lu
e
n
ce
th
e
ac
c
u
r
ac
y
o
f
f
ea
tu
r
e
s
elec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyb
r
id
fea
tu
r
e
s
elec
tio
n
o
f m
icra
o
r
r
a
y
p
r
o
s
ta
te
ca
n
ce
r
…
(
N
u
r
s
a
b
illi
la
h
Mo
h
d
A
li
)
1891
T
ab
le
1
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
b
etwe
en
GA,
PS
O,
an
d
W
OA
S
t
u
d
y
M
e
t
h
o
d
A
c
c
u
r
a
c
y
(
%)
C
o
m
p
u
t
i
o
n
t
i
m
e
(
s)
N
o
.
se
l
e
c
t
e
d
f
e
a
t
u
r
e
s
P
r
o
p
o
se
d
GA
9
1
.
1
7
1
7
.
6
3
4
PSO
9
0
.
5
2
4
.
6
6
4
W
O
A
8
5
.
7
4
4
.
7
4
4
[
2
2
]
K
P
C
A
-
S
V
M
8
4
.
3
1
%
1
8
.
3
1
----
N
o
t
e
*
:
-----
i
n
d
i
c
a
t
e
n
o
t
a
v
a
l
i
a
b
l
e
Fig
u
r
e
8
.
Acc
u
r
ac
y
o
f
o
p
tim
izatio
n
f
o
r
GA,
PS
O
,
an
d
W
OA
B
y
u
s
in
g
cr
o
s
s
o
v
er
,
a
ch
r
o
m
o
s
o
m
e
in
th
e
n
ew
g
en
er
atio
n
c
an
in
h
er
it
th
e
b
est
g
en
etic
c
h
a
r
ac
ter
is
tics
f
r
o
m
th
e
p
ar
en
ts
’
g
en
er
atio
n
.
Mu
tatio
n
is
a
p
r
o
ce
s
s
wh
en
s
o
m
e
o
f
th
e
b
its
(
g
en
es)
in
t
h
e
b
i
n
ar
y
s
tr
in
g
ca
n
f
lip
wh
ich
in
tr
o
d
u
ce
r
an
d
o
m
ch
a
n
g
es
to
t
h
e
o
f
f
s
p
r
in
g
’
s
g
e
n
etic
i
n
f
o
r
m
atio
n
.
T
h
is
m
u
tatio
n
is
i
m
p
o
r
tan
t
b
ec
au
s
e
it
h
elp
s
im
p
r
o
v
in
g
th
e
n
ex
t
g
e
n
e
r
atio
n
an
d
lead
in
g
to
d
iv
er
s
ity
an
d
m
o
r
e
in
n
o
v
ativ
e
s
o
lu
tio
n
s
th
at
m
ig
h
t
n
o
t
b
e
d
is
co
v
er
ed
b
y
o
t
h
er
m
eth
o
d
s
[
2
3
]
.
I
n
DNA
m
icr
o
ar
r
ay
d
ataset,
a
s
et
o
f
f
ea
tu
r
es
with
h
ig
h
f
itn
ess
wil
l
b
e
s
elec
ted
a
s
p
ar
en
ts
an
d
p
er
f
o
r
m
cr
o
s
s
o
v
er
to
y
ield
n
e
w
g
en
er
atio
n
,
wh
ich
is
th
e
s
et
o
f
f
ea
tu
r
es
with
g
r
ea
ter
f
itn
ess
.
W
h
en
th
e
n
u
m
b
er
o
f
g
en
er
atio
n
s
in
cr
ea
s
es,
th
e
ac
cu
r
ac
y
o
f
th
e
s
elec
ted
f
e
atu
r
es
in
cr
ea
s
es,
b
ec
au
s
e
o
n
ly
th
e
m
o
s
t
u
s
ef
u
l
f
ea
tu
r
es
ar
e
r
etain
e
d
in
th
e
n
ew
g
en
er
atio
n
an
d
th
e
less
u
s
ef
u
l
f
ea
tu
r
es
alr
ea
d
y
elim
i
n
ated
.
Mo
r
e
o
v
er
,
a
m
u
tatio
n
o
cc
u
r
s
o
n
th
e
s
elec
ted
f
ea
tu
r
es
in
ea
c
h
g
en
er
ati
o
n
.
T
h
is
is
cr
u
cial
to
en
s
u
r
e
th
e
d
iv
er
s
ity
a
n
d
ac
cu
r
ac
y
o
f
s
o
lu
tio
n
s
p
r
o
d
u
ce
d
,
wh
ich
is
th
e
f
ea
t
u
r
e
s
elec
ted
.
3
.
5
.
Num
ber
o
f
s
elec
t
ed
f
ea
t
ures
T
ab
le
2
d
is
p
lay
s
th
e
n
u
m
b
er
o
f
g
en
es
s
elec
ted
b
y
GA,
PS
O
,
an
d
W
OA
.
As
an
ticip
ated
,
GAs,
as
m
etah
eu
r
is
tic
ap
p
r
o
a
ch
es,
ten
d
to
o
u
t
p
er
f
o
r
m
o
t
h
er
alg
o
r
ith
m
s
co
n
s
tr
ain
ed
b
y
is
s
u
es
lik
e
lo
ca
l
m
in
im
a
.
Fro
m
th
e
em
p
lo
y
ed
o
p
tim
izer
,
th
e
n
u
m
b
er
o
f
s
elec
ted
g
en
es
is
4
.
Fo
r
m
th
e
af
f
y
m
etr
ic
f
ea
tu
r
es,
ca
n
b
e
u
s
ed
to
id
en
tify
th
e
g
e
n
e
an
d
s
y
m
b
o
l
f
o
r
p
r
o
s
tate
ca
n
ce
r
[
2
4
]
.
T
h
ese
s
elec
ted
f
ea
tu
r
es
ca
n
b
e
f
u
r
th
er
a
n
aly
s
ed
as
p
o
ten
tial b
io
m
ar
k
er
s
f
o
r
p
atie
n
t ta
r
g
eted
th
e
r
ap
y
[
2
5
]
−
[
2
7
]
.
T
ab
le
2
.
Selecte
d
g
e
n
e
n
am
e
f
o
r
GA,
PS
O,
an
d
W
OA
GA
P
S
O
W
O
A
G
e
n
e
a
f
f
y
me
t
r
i
x
G
e
n
e
n
a
me
G
e
n
e
a
f
f
y
me
t
r
i
x
G
e
n
e
n
a
me
G
e
n
e
a
f
f
y
me
t
r
i
x
G
e
n
e
n
a
me
3
2
5
9
8
_
a
t
n
e
u
r
a
l
EG
F
L
l
i
k
e
2
(
N
ELL
2
)
3
2
5
9
8
_
a
t
n
e
u
r
a
l
EG
F
L
l
i
k
e
2
(
N
ELL
2
)
3
7
3
9
4
_
a
t
c
o
m
p
l
e
me
n
t
C
7
(
C
7
)
3
8
4
0
6
_
f
_
a
t
p
r
o
s
t
a
g
l
a
n
d
i
n
D
2
sy
n
t
h
a
se
(
P
TG
D
S
)
3
7
3
6
6
_
a
t
P
D
Z
a
n
d
LI
M
d
o
ma
i
n
5
(
P
D
LI
M
5
)
3
8
4
0
6
_
f
_
a
t
p
r
o
s
t
a
g
l
a
n
d
i
n
D
2
s
y
n
t
h
a
se
(
P
TG
D
S
)
3
8
6
3
4
_
a
t
r
e
t
i
n
o
l
b
i
n
d
i
n
g
p
r
o
t
e
i
n
1
(
R
B
P
1
)
3
8
6
3
4
_
a
t
r
e
t
i
n
o
l
b
i
n
d
i
n
g
p
r
o
t
e
i
n
1
(
R
B
P
1
)
3
9
0
5
4
_
a
t
g
l
u
t
a
t
h
i
o
n
e
S
-
t
r
a
n
sf
e
r
a
se
mu
1
(
G
S
TM
1
)
3
9
0
5
4
_
a
t
g
l
u
t
a
t
h
i
o
n
e
S
-
t
r
a
n
sf
e
r
a
se
mu
1
(
G
S
TM
1
)
3
9
0
5
4
_
a
t
g
l
u
t
a
t
h
i
o
n
e
S
-
t
r
a
n
sf
e
r
a
se
mu
1
(
G
S
TM
1
)
4
0
2
8
2
_
s
_
a
t
c
o
m
p
l
e
me
n
t
f
a
c
t
o
r
D
(
C
F
D
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
3
,
Dec
em
b
er
20
24
:
1
8
8
4
-
1
8
9
4
1892
4.
CO
NCLU
SI
O
N
Pro
s
tate
ca
n
ce
r
is
a
lead
in
g
ca
u
s
e
o
f
d
ea
th
am
o
n
g
m
e
n
,
h
ig
h
lig
h
tin
g
t
h
e
im
p
o
r
tan
c
e
o
f
ea
r
ly
id
en
tific
atio
n
.
R
o
b
u
s
t
o
p
ti
m
izer
s
an
d
class
if
ier
s
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
p
r
o
s
tate
clas
s
if
icatio
n
.
Hy
b
r
id
m
eth
o
d
th
at
u
s
es
m
et
ah
eu
r
is
tic
as
f
ea
tu
r
e
s
elec
tio
n
g
e
n
er
ally
o
u
tp
e
r
f
o
r
m
o
t
h
er
m
eth
o
d
s
.
A
m
aj
o
r
ch
allen
g
e
is
co
m
b
in
in
g
th
e
m
o
s
t
ac
cu
r
ate
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
with
th
e
class
if
ier
m
o
d
el.
T
o
ad
d
r
ess
th
is
,
a
h
y
b
r
i
d
R
elief
-
GA
an
d
SVM
h
as
b
ee
n
p
r
o
p
o
s
ed
f
o
r
ac
c
u
r
ate
p
r
o
s
tate
ca
n
ce
r
class
if
icatio
n
.
Fu
r
th
er
m
o
r
e,
th
e
m
o
d
el
id
en
tifie
s
f
o
u
r
s
elec
ted
f
ea
tu
r
es,
wh
ich
ca
n
b
e
f
u
r
th
er
an
aly
ze
d
f
o
r
b
io
m
ar
k
er
d
is
co
v
er
y
.
T
h
e
GA
was
d
eter
m
in
ed
to
b
e
th
e
m
o
s
t
ef
f
ec
tiv
e
o
p
tim
izer
in
h
y
b
r
id
f
ea
tu
r
e
s
elec
tio
n
,
as
it
ac
h
iev
ed
b
etter
ac
c
u
r
ac
y
th
an
b
o
th
PS
O
an
d
W
OA.
F
u
tu
r
e
wo
r
k
will
ass
ess
th
e
ap
p
licab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
ac
r
o
s
s
d
iv
er
s
e
d
is
ea
s
e
h
ig
h
d
im
en
s
io
n
al
d
ata
s
ets f
o
r
co
m
p
r
eh
e
n
s
iv
e
v
alid
at
io
n
.
Ad
d
itio
n
ally
,
ef
f
o
r
ts
will f
o
cu
s
o
n
e
n
h
an
ci
n
g
m
o
d
el
p
er
f
o
r
m
an
ce
b
y
ex
p
lo
r
in
g
o
th
er
m
etah
eu
r
is
tic
as
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
an
d
c
o
n
s
id
er
in
g
h
y
p
er
p
ar
am
eter
tu
n
in
g
in
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
tack
le
s
tr
en
g
th
an
d
ca
p
a
city
ch
allen
g
es
o
n
lar
g
er
d
atasets
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
wo
r
k
is
s
u
p
p
o
r
ted
b
y
Un
iv
er
s
iti
T
ek
n
ik
al
Ma
lay
s
ia
Me
lak
a
an
d
Mu
ltime
d
ia
Un
iv
er
s
ity
,
Ma
lay
s
ia.
Sp
ec
ial
th
an
k
s
to
Mr
.
L
o
h
Yi
C
o
n
g
f
o
r
h
is
v
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with
th
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Me
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k
.
RE
F
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R
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NC
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S
[
1
]
M
.
S
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o
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,
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.
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0
.
[
2
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Y
.
J.
Ta
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,
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m,
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[
3
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P
.
Esma
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h
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mers
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[
4
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5
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D
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[
6
]
P
.
M
o
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l
a
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[
7
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M
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[
8
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2
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3
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[
9
]
A
.
K
.
S
h
u
k
l
a
,
S
.
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.
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,
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d
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,
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d
D
.
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p
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,
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t
&
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,
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A
.
C
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.
,
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su
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s,”
I
EEE
/
A
C
M
T
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s
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c
t
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s
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B
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Bi
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v
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(
4
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p
p
.
1
1
0
6
–
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1
1
9
,
2
0
1
2
.
[
1
1
]
N
.
A
.
Z
a
mr
i
,
N
.
A
.
A
b
.
A
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,
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.
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.
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,
a
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d
A
.
K
.
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h
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,
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e
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t
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o
f
mi
c
r
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d
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k
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w
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t
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m
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t
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t
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p
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3
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3
3
9
0
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r
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1
0
8
2
4
0
9
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[
1
2
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N
.
M
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A
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,
R
.
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e
sar,
a
n
d
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.
A
.
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b
.
A
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z
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y
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c
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r
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me
t
a
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t
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c
m
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:
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c
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n
s
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v
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v
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e
w
,
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m
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ry
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v
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4
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1
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[
1
3
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N
.
M
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h
d
A
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i
,
N
.
A
.
A
b
A
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z
,
a
n
d
R
.
B
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,
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mi
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in
1
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.
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in
2
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a
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d
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2
,
re
sp
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c
ti
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ly
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.
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is
re
se
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a
b
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it
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ste
m
s,
c
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tr
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st
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m
,
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p
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m
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d
m
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in
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
firi
n
@
u
tem
.
e
d
u
.
m
y
.
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