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[
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[
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6
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w
i
n
g
c
o
m
p
l
e
x
i
ty
o
f
t
h
e
p
r
o
b
l
e
m
,
es
p
e
ci
a
l
l
y
wi
t
h
t
h
e
i
n
c
l
u
s
i
o
n
o
f
u
n
c
e
r
t
a
i
n
t
i
es
i
n
t
o
t
h
e
s
y
s
te
m
,
w
h
i
c
h
m
a
y
e
x
c
e
e
d
t
h
e
c
a
p
a
b
i
l
i
ti
e
s
o
f
c
o
n
v
e
n
t
i
o
n
a
l
a
l
g
o
r
i
t
h
m
s
[
1
1
]
.
T
h
e
s
e
a
l
g
o
r
i
t
h
m
s
a
r
e
h
i
g
h
l
y
u
s
e
d
i
n
t
h
e
m
e
d
i
c
al
f
ie
l
d
,
f
o
r
e
x
a
m
p
l
e,
Sab
ir
i
et
a
l.
in
[
1
2
]
a
m
o
d
if
ied
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PSO
)
alg
o
r
ith
m
is
u
s
ed
to
m
ax
im
ize
elec
tr
o
d
e'
s
s
en
s
itiv
ity
an
d
m
in
im
ize
c
ut
-
o
f
f
f
r
eq
u
en
cy
,
Sab
ir
i
et
a
l.
in
[
1
3
]
u
s
ed
ar
tif
icial
b
ee
co
lo
n
y
(
AB
C
)
alg
o
r
ith
m
to
o
p
tim
ize
a
c
o
m
p
lem
e
n
tar
y
m
etal
-
o
x
id
e
-
s
em
ico
n
d
u
cto
r
(
C
MO
S
)
cu
r
r
e
n
t m
o
d
e
i
n
s
tr
u
m
en
tatio
n
a
m
p
l
if
ier
f
o
r
b
io
m
ed
ical
a
p
p
licatio
n
s
.
Nu
m
er
o
u
s
r
esear
ch
h
as
b
ee
n
d
o
n
e
s
p
ec
if
ically
in
th
e
f
ield
o
f
h
ea
r
t
d
is
ea
s
e
p
r
ed
ictio
n
.
A
cc
o
r
d
in
g
t
o
A
l
B
a
t
ai
n
e
h
a
n
d
Ma
n
a
c
e
k
[
1
4
]
,
a
n
m
u
l
t
i
-
l
a
y
e
r
p
e
r
c
e
p
t
r
o
n
(
MLP
)
-
PS
O
a
l
g
o
r
i
t
h
m
i
s
p
r
o
p
o
s
e
d
t
o
p
r
e
d
i
c
t
h
ea
r
t
d
i
s
e
as
e
u
s
i
n
g
t
h
e
C
l
e
v
e
l
a
n
d
h
e
a
r
t
d
i
s
e
as
e
d
a
t
a
s
e
t
(
C
H
DD
)
a
n
d
c
o
m
p
a
r
e
d
i
t
t
o
o
t
h
e
r
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
.
P
SO
is
u
s
e
d
i
n
t
h
e
t
r
a
i
n
i
n
g
p
h
as
e
t
o
f
i
n
d
we
i
g
h
t
s
t
h
a
t
m
i
n
i
m
i
z
e
t
h
e
e
r
r
o
r
f
u
n
c
t
i
o
n
a
s
t
h
e
o
p
t
i
m
i
z
a
ti
o
n
o
b
j
e
c
ti
v
e
o
f
t
h
e
M
L
P
n
et
w
o
r
k
,
a
n
d
t
h
i
s
t
ec
h
n
i
q
u
e
o
u
t
p
e
r
f
o
r
m
e
d
o
t
h
e
r
t
e
s
t
ed
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
.
C
h
a
n
d
r
a
s
e
k
h
a
r
a
n
d
P
e
d
d
a
k
r
i
s
h
n
a
[
1
5
]
u
s
e
d
G
r
id
S
e
a
r
c
h
C
V
w
it
h
f
i
v
e
-
f
o
l
d
c
r
o
s
s
-
v
a
li
d
a
t
i
o
n
f
o
r
s
i
x
m
a
c
h
i
n
e
l
ea
r
n
i
n
g
a
l
g
o
r
it
h
m
s
h
y
p
e
r
p
a
r
a
m
e
t
e
r
s
o
p
ti
m
i
z
at
i
o
n
.
T
h
e
s
o
f
t
v
o
t
i
n
g
e
n
s
e
m
b
l
e
c
l
ass
i
f
i
e
r
c
o
m
b
i
n
i
n
g
t
h
e
s
i
x
al
g
o
r
i
t
h
m
s
o
u
t
p
e
r
f
o
r
m
e
d
l
o
g
i
s
ti
c
r
e
g
r
es
s
i
o
n
a
n
d
A
d
a
B
o
o
s
t
cl
a
s
s
i
f
ie
r
o
n
C
l
e
v
el
a
n
d
a
n
d
I
E
E
E
D
at
a
p
o
r
t
d
a
t
a
s
et
s
.
O
z
c
a
n
a
n
d
P
e
k
e
r
[
1
6
]
e
m
p
l
o
y
e
d
c
l
a
s
s
i
f
i
c
at
i
o
n
a
n
d
r
e
g
r
e
s
s
i
o
n
t
r
ee
(
C
A
R
T
)
s
u
p
e
r
v
i
s
e
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
e
t
h
o
d
t
o
p
r
e
d
i
c
t
h
e
ar
t
d
i
s
e
as
e
w
h
i
c
h
h
a
s
s
h
o
w
n
g
r
e
a
t
r
e
s
u
lt
s
,
t
h
e
d
ec
is
i
o
n
r
u
l
e
s
w
er
e
e
x
t
r
a
c
t
e
d
t
o
r
a
n
k
t
h
e
f
e
a
t
u
r
es
b
a
s
e
d
o
n
i
m
p
o
r
t
a
n
c
e
i
n
o
r
d
e
r
t
o
s
i
m
p
l
i
f
y
t
h
e
u
s
e
f
o
r
c
l
i
n
i
c
al
p
u
r
p
o
s
e
s
.
O
g
u
n
d
e
p
o
a
n
d
Y
a
h
y
a
[
1
7
]
c
o
n
s
i
d
e
r
e
d
b
o
t
h
C
l
e
v
e
l
a
n
d
d
a
ta
s
e
t
f
o
r
b
u
i
l
d
i
n
g
c
l
a
s
s
i
f
i
ca
t
i
o
n
m
o
d
e
l
s
a
n
d
t
h
e
S
t
a
t
l
o
g
d
a
t
a
f
o
r
r
e
s
u
l
t
s
v
a
li
d
a
t
i
o
n
.
S
o
m
e
o
f
t
h
e
b
i
o
-
c
li
n
i
c
al
c
a
teg
o
r
i
c
a
l
v
a
r
i
a
b
l
es
a
r
e
f
o
u
n
d
t
o
b
e
s
t
r
o
n
g
l
y
as
s
o
ci
a
t
e
d
w
i
th
t
h
e
h
e
a
r
t
d
is
e
as
e
c
o
n
d
i
t
i
o
n
s
o
f
t
h
e
p
a
t
i
e
n
ts
,
a
n
d
t
h
e
s
u
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
s
(
S
V
M
)
a
c
h
ie
v
e
d
b
e
s
t
p
r
e
d
ic
t
i
v
e
p
e
r
f
o
r
m
a
n
c
e
s
c
o
m
p
a
r
e
d
t
o
t
h
e
o
t
h
e
r
t
e
s
t
e
d
al
g
o
r
i
t
h
m
s
.
R
es
e
a
r
c
h
b
y
G
u
p
t
a
a
n
d
S
e
d
a
m
k
a
r
[
1
8
]
,
g
e
n
et
i
c
a
lg
o
r
i
t
h
m
i
s
u
s
e
d
f
o
r
f
e
a
t
u
r
e
s
el
e
c
ti
o
n
a
n
d
h
y
p
e
r
p
a
r
a
m
e
t
e
r
t
u
n
i
n
g
o
n
b
o
t
h
S
V
M
a
n
d
n
e
u
r
a
l
n
e
t
w
o
r
k
(
NN
)
f
o
r
h
e
a
r
t d
i
s
e
as
e
p
r
e
d
i
ct
i
o
n
.
C
a
n
d
γ
a
r
e
t
h
e
o
p
ti
m
i
z
e
d
p
a
r
am
e
t
e
r
s
i
n
r
a
d
i
a
l
b
as
is
f
u
n
c
t
i
o
n
(
R
B
F
)
k
e
r
n
e
l
f
o
r
SV
M
,
w
h
il
e
n
o
.
o
f
h
i
d
d
e
n
l
a
y
e
r
s
,
n
o
.
h
i
d
d
e
n
n
o
d
e
s
,
l
ea
r
n
i
n
g
r
a
t
e
m
o
m
e
n
t
u
m
,
a
n
d
o
p
t
i
m
i
z
e
r
a
r
e
t
h
e
t
u
n
e
d
p
a
r
a
m
e
t
e
r
s
i
n
M
L
P
N
N
cl
a
s
s
i
f
i
e
r
.
T
h
e
r
e
s
u
l
ts
w
e
r
e
b
e
t
te
r
t
h
a
n
u
t
i
l
i
zi
n
g
G
r
e
a
d
s
e
a
r
c
h
f
o
r
t
h
e
s
a
m
e
.
T
h
i
s
w
o
r
k
a
i
m
s
t
o
e
v
a
l
u
at
e
h
e
a
r
t
d
i
s
e
as
e
p
r
e
d
i
c
t
i
o
n
p
e
r
f
o
r
m
a
n
c
e
s
b
y
a
n
a
l
y
z
i
n
g
th
e
i
m
p
a
c
t
o
f
e
a
c
h
s
t
e
p
o
f
th
e
a
p
p
r
o
a
c
h
:
p
r
e
-
p
r
o
c
e
s
s
i
n
g
,
f
e
a
t
u
r
e
s
s
e
l
ec
t
i
o
n
,
m
e
t
a
h
e
u
r
i
s
t
i
cs
v
a
li
d
a
t
i
o
n
a
n
d
h
y
p
e
r
p
a
r
a
m
e
t
e
r
s
t
u
n
i
n
g
c
o
m
p
a
r
i
n
g
t
h
r
e
e
d
i
f
f
e
r
e
n
t
al
g
o
r
i
t
h
m
s
(
P
S
O
,
g
r
e
y
w
o
l
f
o
p
t
i
m
i
z
e
r
(
GW
O
)
,
d
i
f
f
e
r
e
n
t
i
a
l
e
v
o
l
u
t
i
o
n
(
DE
)
)
o
n
b
o
t
h
k
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
s
(
K
N
N
)
a
n
d
SV
M
c
l
a
s
s
i
f
ie
r
s
.
T
h
e
p
a
p
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws
:
s
ec
tio
n
2
h
ig
h
lig
h
ts
th
e
d
if
f
e
r
en
t
s
tep
s
o
f
th
e
ap
p
r
o
ac
h
.
S
ec
tio
n
3
ex
p
o
s
es
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
o
f
th
e
u
s
ed
m
eth
o
d
in
ad
d
itio
n
to
th
e
m
eta
h
eu
r
is
tics
v
alid
atio
n
o
n
k
n
o
wn
f
u
n
ctio
n
s
.
F
in
ally
,
a
co
n
clu
s
io
n
o
f
th
is
s
tu
d
y
is
g
iv
en
in
s
ec
tio
n
4
o
u
tlin
in
g
p
r
o
m
is
in
g
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
c
h
.
2.
M
E
T
H
O
D
I
n
th
is
s
ec
tio
n
,
th
e
d
if
f
er
en
t
s
tep
s
f
r
o
m
d
ata
c
o
llectio
n
t
o
h
y
p
er
p
ar
am
eter
s
t
u
n
in
g
ar
e
e
x
p
lo
r
ed
to
ex
p
o
s
e
h
o
w
th
e
s
tu
d
y
was
co
n
d
u
cted
.
First,
th
e
C
HDD
an
d
th
e
u
s
ed
p
r
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
ar
e
d
etailed
.
T
h
en
t
h
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
an
d
th
e
m
etah
e
u
r
is
tic
alg
o
r
ith
m
s
a
r
e
p
r
esen
ted
,
to
f
in
ally
ex
p
lain
h
o
w
th
e
h
y
p
er
p
ar
am
eter
tu
n
in
g
is
p
e
r
f
o
r
m
ed
.
2
.
1
.
Da
t
a
s
et
co
llect
i
o
n
Fo
r
an
aly
zin
g
c
o
r
o
n
ar
y
h
ea
r
t
d
is
ea
s
e,
th
e
C
HDD
i
s
wid
ely
co
n
s
id
er
ed
th
e
s
tan
d
a
r
d
r
e
f
er
e
n
ce
[
1
9
]
,
1
3
r
is
k
f
ac
to
r
s
am
o
n
g
7
6
p
r
es
en
ted
b
y
th
e
d
ataset
as
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ein
g
ass
o
ciate
d
to
h
ea
r
t
d
is
ea
s
e,
ar
e
id
en
tifie
d
to
b
e
as
s
ig
n
if
ican
t
co
n
tr
ib
u
to
r
s
[
2
0
]
.
Acc
o
r
d
in
g
to
th
e
UC
I
r
v
in
e
Ma
ch
in
e
L
ea
r
n
in
g
R
ep
o
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ito
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y
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e
d
ataset
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m
p
r
is
es r
ec
o
r
d
s
f
o
r
3
0
3
p
ati
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ts
in
clu
d
in
g
1
3
in
p
u
t f
ea
tu
r
e
s
f
o
r
ea
ch
p
atien
t :
ag
e,
s
ex
,
ch
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ain
ty
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e
(
cp
)
,
r
esti
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lo
o
d
p
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r
e
(
tr
estb
p
s
)
,
s
er
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m
ch
o
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er
o
l
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ch
o
l
)
,
f
asti
n
g
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l
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o
d
s
u
g
ar
(
f
b
s
)
,
r
esti
n
g
elec
tr
o
ca
r
d
io
g
r
ap
h
ic
r
esu
lts
(
r
estecg
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,
m
ax
im
u
m
h
ea
r
t
r
at
e
ac
h
iev
ed
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th
alac
h
)
,
e
x
er
cise
in
d
u
ce
d
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g
in
a
(
ex
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g
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ST
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ep
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ess
io
n
in
d
u
ce
d
b
y
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er
cise
r
elativ
e
to
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est
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o
ld
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ea
k
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s
lo
p
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th
e
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k
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er
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ST
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eg
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en
t
(
s
lo
p
e)
,
n
u
m
b
e
r
o
f
m
ajo
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v
e
s
s
els
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lo
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ed
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y
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lu
o
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o
s
co
p
y
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ca
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t
h
alliu
m
s
tr
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test
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lt
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d
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o
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tp
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t/tar
g
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h
ich
r
e
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r
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ts
h
ea
r
t
d
is
ea
s
e
p
r
esen
ce
(
Yes
o
r
No
)
.
T
h
e
d
ataset
co
n
tain
s
b
o
th
ca
teg
o
r
ical
an
d
n
u
m
er
ical
d
ata.
2
.
2
.
P
re
pro
ce
s
s
ing
a
nd
f
ea
t
u
re
s
s
elec
t
io
n
Data
will
co
n
tain
er
r
o
r
s
,
o
u
t
o
f
r
an
g
e
v
alu
es,
im
p
o
s
s
ib
le
d
at
a
co
m
b
in
atio
n
s
,
m
is
s
in
g
v
alu
e
s
o
r
m
o
s
t
s
u
b
s
tan
tially
,
d
ata
is
n
o
t
s
u
itab
le
to
s
tar
t
a
d
ata
m
i
n
in
g
p
r
o
ce
s
s
[
2
1
]
,
th
e
f
u
n
d
am
en
ta
l
o
b
jectiv
e
o
f
d
ata
p
r
ep
ar
atio
n
is
to
in
cr
ea
s
e
th
e
d
ata'
s
co
r
r
ec
tn
ess
an
d
q
u
ality
s
o
th
at
it
is
m
o
r
e
s
u
ited
f
o
r
an
aly
s
is
[
2
2
]
.
I
n
o
u
r
ca
s
e
s
tu
d
y
,
th
e
d
ataset
is
d
iv
id
ed
in
to
n
u
m
er
ical
a
n
d
ca
te
g
o
r
ical
f
ea
tu
r
es,
to
em
p
l
o
y
s
tan
d
ar
d
izatio
n
an
d
one
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h
o
t
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co
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in
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tech
n
iq
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es
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n
th
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two
ty
p
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f
f
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tu
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cc
ess
iv
ely
,
th
en
th
e
f
ea
tu
r
es
s
elec
tio
n
tech
n
iq
u
e
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at
is
s
u
itab
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f
o
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m
ac
h
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lear
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m
o
d
els.
Featu
r
es
s
elec
tio
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aim
s
to
id
en
tify
th
e
m
o
s
t
r
elev
a
n
t
f
ea
tu
r
e
s
f
o
r
m
ac
h
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e
lear
n
in
g
m
o
d
el
an
d
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m
o
d
el
p
e
r
f
o
r
m
an
ce
im
p
r
o
v
e
m
en
t,
m
o
d
el
co
m
p
lex
ity
a
n
d
tr
ain
in
g
tim
e
r
ed
u
ctio
n
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
a
wr
ap
p
er
m
eth
o
d
d
ir
ec
tly
d
ep
en
d
s
o
n
th
e
u
s
ed
class
if
ier
,
s
in
ce
th
e
v
a
r
iab
les
s
elec
tio
n
is
d
o
n
e
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
p
er
f
o
r
m
a
n
ce
s
with
d
if
f
er
e
n
t
s
u
b
s
ets
o
f
f
ea
tu
r
es
[
2
3
]
.
I
n
ter
m
s
o
f
ac
cu
r
ac
y
,
th
e
wr
ap
p
er
s
tr
ateg
y
ca
n
o
u
tp
er
f
o
r
m
t
h
e
f
ilter
ap
p
r
o
ac
h
[
2
4
]
.
Fig
u
r
e
1
p
r
esen
ts
a
co
n
ce
p
tu
al
d
iag
r
am
o
f
t
h
e
wr
ap
p
er
m
eth
o
d
f
o
r
f
ea
t
u
r
e
s
elec
tio
n
.
I
n
o
u
r
s
tu
d
y
,
we
s
p
ec
if
ically
u
s
ed
b
ac
k
war
d
el
im
in
atio
n
wr
ap
p
er
m
eth
o
d
,
w
h
ich
co
n
s
is
ts
in
s
tar
tin
g
with
all
th
e
f
ea
tu
r
es,
a
n
d
r
em
o
v
es
th
e
least
s
ig
n
if
ican
t
o
n
e
at
ea
ch
iter
atio
n
u
n
til a
s
to
p
p
in
g
cr
iter
io
n
is
m
e
t.
T
h
e
m
ain
s
tep
s
ar
e:
−
Fo
r
m
in
g
a
p
o
o
l
o
f
ca
n
d
id
ate
s
u
b
s
ets
.
−
Mo
d
el
tr
ain
in
g
f
o
r
ea
ch
s
u
b
s
et
an
d
ev
al
u
atio
n
b
ased
o
n
th
e
c
h
o
s
en
m
etr
ic.
−
Su
b
s
et
s
elec
tio
n
b
ased
o
n
th
e
p
er
f
o
r
m
an
ce
o
n
v
alid
atio
n
s
et.
Fig
u
r
e
1
.
W
r
ap
p
er
m
eth
o
d
p
r
o
ce
s
s
2
.
3
.
M
a
chine le
a
rning
m
o
de
ls
Ma
ch
in
e
lear
n
in
g
is
th
e
f
ield
d
ed
icate
d
to
d
e
v
elo
p
in
g
co
m
p
u
ter
alg
o
r
ith
m
s
th
at
g
i
v
es
co
m
p
u
ter
s
th
e
ca
p
ac
ity
to
m
ak
e
p
r
ed
ictio
n
s
b
y
lear
n
in
g
f
r
o
m
d
ata
an
d
p
a
s
t
ex
p
er
ien
ce
s
with
o
u
t
h
u
m
a
n
in
v
o
lv
em
e
n
t
[
2
5
]
.
I
n
th
is
s
tu
d
y
we
h
av
e
ch
o
s
en
KNN
an
d
SVM.
T
h
e
f
ir
s
t
alg
o
r
ith
m
class
if
ies
n
ew
d
ata
p
o
in
ts
b
y
f
in
d
i
n
g
th
e
'
k
'
clo
s
est
tr
ain
in
g
d
ata
p
o
in
ts
(
n
eig
h
b
o
r
s
)
to
th
e
q
u
er
y
b
ein
g
t
ested
,
th
en
u
s
es
th
ese
n
eig
h
b
o
r
s
to
d
eter
m
in
e
th
e
class
if
icatio
n
[
2
6
]
.
On
th
e
o
th
er
h
an
d
,
in
SVM,
d
ata
p
o
in
ts
wh
ich
ar
e
clo
s
est
to
th
e
h
y
p
er
p
lan
e
ar
e
k
n
o
wn
as
s
u
p
p
o
r
t
v
ec
t
o
r
s
,
an
d
th
e
m
ar
g
in
r
ep
r
esen
ts
th
e
d
is
tan
ce
f
r
o
m
th
e
h
y
p
er
p
lan
e
t
o
th
e
clo
s
est
d
ata
p
o
in
t
f
r
o
m
eith
er
o
f
th
e
class
es
it
'
s
s
ep
ar
atin
g
.
T
h
e
p
r
im
ar
y
g
o
al
is
to
id
en
tify
th
e
h
y
p
er
p
lan
e
th
at
cr
ea
tes
th
e
lar
g
est
p
o
s
s
ib
le
m
ar
g
in
to
i
n
cr
ea
s
e
th
e
p
r
o
b
a
b
ilit
y
th
at
n
ew,
u
n
s
ee
n
d
ata
p
o
in
ts
will b
e
class
if
ied
co
r
r
ec
tly
[
2
7
]
.
2
.
4
.
M
et
a
heuris
t
ics
PS
O,
G
W
O
,
an
d
DE
alg
o
r
ith
m
ar
e
th
e
s
elec
ted
alg
o
r
ith
m
s
f
o
r
tu
n
in
g
in
th
is
s
tu
d
y
.
T
h
eir
m
ain
o
b
jectiv
e
is
to
ex
p
lo
r
e
th
e
s
ea
r
ch
s
p
ac
e,
g
en
e
r
ate
s
o
lu
tio
n
s
a
n
d
e
v
alu
ate
th
ei
r
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
2
d
escr
ib
es
th
e
m
etah
eu
r
is
tics
d
u
r
i
n
g
th
e
h
y
p
er
p
ar
am
eter
t
u
n
in
g
p
r
o
ce
s
s
,
b
eg
in
n
in
g
f
r
o
m
d
ata
p
r
e
p
r
o
ce
s
s
in
g
,
to
th
e
b
est
s
o
lu
tio
n
s
elec
tio
n
,
with
th
e
n
u
m
b
er
o
f
iter
atio
n
s
as th
e
s
to
p
p
in
g
cr
iter
io
n
.
Velo
city
u
p
d
ate
is
a
cr
u
cial
s
tep
in
t
h
e
PS
O
alg
o
r
ith
m
'
s
s
ea
r
ch
p
r
o
ce
s
s
.
E
ac
h
p
ar
ticle'
s
n
ew
p
o
s
itio
n
is
d
y
n
am
ically
d
eter
m
in
e
d
b
y
co
m
b
i
n
in
g
its
cu
r
r
e
n
t
v
elo
cit
y
,
its
in
d
iv
i
d
u
al
b
est
-
f
o
u
n
d
p
o
s
itio
n
,
an
d
th
e
b
est
p
o
s
itio
n
d
is
co
v
er
ed
b
y
th
e
en
tire
s
war
m
.
T
h
is
in
teg
r
atio
n
o
f
lo
ca
l
an
d
g
lo
b
al
in
f
o
r
m
atio
n
en
ab
les
ef
f
ec
tiv
e
ex
p
lo
r
atio
n
an
d
e
x
p
lo
itatio
n
o
f
th
e
s
ea
r
ch
s
p
ac
e
.
T
h
e
GW
O
alg
o
r
ith
m
s
im
u
late
th
e
lead
er
s
h
ip
s
tr
u
ctu
r
e
a
n
d
h
u
n
tin
g
b
e
h
av
io
r
o
f
g
r
e
y
wo
lv
es
o
b
s
er
v
ed
in
n
atu
r
e
.
Fo
u
r
d
is
tin
ct
class
es
o
f
g
r
ey
wo
lv
es
alp
h
a,
b
eta,
d
elta,
an
d
o
m
eg
a
a
r
e
u
tili
ze
d
to
r
ep
r
esen
t
th
e
h
ier
ar
ch
y
o
f
lead
er
s
h
ip
.
I
n
th
e
u
p
d
ate
p
r
o
ce
s
s
,
th
e
alg
o
r
ith
m
allo
ws
its
s
ea
r
ch
ag
en
ts
to
u
p
d
ate
th
eir
p
o
s
itio
n
b
ased
o
n
th
e
lo
ca
tio
n
o
f
th
e
alp
h
a,
b
eta,
a
n
d
d
elta
wo
lv
es; an
d
attac
k
to
war
d
s
th
e
p
r
ey
[
2
8
]
.
I
n
t
h
e
D
E
m
e
t
h
o
d
,
t
h
e
i
n
i
t
i
a
l
p
o
p
u
l
a
t
i
o
n
i
s
c
r
e
a
te
d
b
y
r
a
n
d
o
m
l
y
s
e
l
e
ct
i
n
g
v
a
l
u
e
s
f
o
r
e
a
c
h
v
a
r
i
a
b
l
e
,
t
h
e
l
o
w
e
r
a
n
d
u
p
p
e
r
b
o
u
n
d
s
a
r
e
d
e
f
i
n
e
d
b
y
t
h
e
u
s
e
r
b
as
e
d
o
n
t
h
e
s
p
e
ci
f
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s
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les
[
3
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8
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Hea
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t d
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ith
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4335
class
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[
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u
r
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3
s
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o
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if
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er
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t
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s
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ase
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e
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lated
to
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ate
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e
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o
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el’
s
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m
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ce
.
W
h
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c
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e
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t
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to
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m
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f
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h
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u
r
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2
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Flo
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PS
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GW
O
an
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DE
alg
o
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ith
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s
Fig
u
r
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3
.
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v
alu
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p
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ase
3.
RE
SU
L
T
S AN
D
D
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SCU
SS
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N
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n
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d
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s
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ed
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p
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tag
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3
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1
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1
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p
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x
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ately
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2
1
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wh
ile
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s
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as
s
h
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wn
i
n
T
ab
le
2
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n
th
e
f
ir
s
t
s
tep
,
aim
in
g
f
o
r
t
h
e
r
em
o
v
al
o
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j
u
s
t
o
n
e
f
ea
tu
r
e,
'C
h
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s
id
en
tifie
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as
th
e
o
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tim
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ch
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ice.
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en
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th
e
m
eth
o
d
was
r
e
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a
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p
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e
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an
d
it
c
o
n
v
er
g
ed
o
n
'
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h
o
l'
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d
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r
est
b
p
s
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as
th
e
s
elec
ted
p
air
f
o
r
r
em
o
v
al.
T
ab
le
2
.
Featu
r
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im
p
ac
t
A
l
g
o
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t
h
ms
A
l
l
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e
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t
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s
1
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e
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t
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r
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2
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e
m
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t
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2
2
5
7
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1
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1
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8
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4
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1
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n
ad
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itio
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e
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lig
h
t
im
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en
t
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n
th
e
e
r
r
o
r
as
s
o
m
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f
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tu
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r
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ed
,
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s
ig
n
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ican
t
b
en
ef
it
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e
r
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ltin
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r
ea
s
e
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m
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d
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m
p
lex
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.
T
h
is
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ed
u
ctio
n
in
co
m
p
le
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ity
d
ir
e
ctly
tr
an
s
lates
in
to
f
aster
tr
ain
in
g
tim
es,
m
ak
in
g
th
e
m
o
d
el
m
o
r
e
co
m
p
u
tatio
n
a
lly
ef
f
icien
t.
Fu
r
th
er
m
o
r
e,
a
l
ess
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m
p
lex
m
o
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el
o
f
ten
ex
h
ib
its
b
etter
g
en
e
r
aliza
tio
n
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p
ab
ilit
ies,
im
p
r
o
v
in
g
it
s
p
er
f
o
r
m
a
n
ce
o
n
u
n
s
ee
n
d
ata.
3
.
2
.
M
et
a
heuris
t
ic
a
lg
o
rit
h
m
s
v
a
lid
a
t
io
n
Me
tah
eu
r
is
tic
alg
o
r
ith
m
s
v
alid
atio
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is
a
cr
u
cial
s
tep
th
at
p
r
ec
ed
es
th
eir
ap
p
licatio
n
t
o
th
e
ac
tu
al
p
r
o
b
lem
.
Sp
h
er
e
,
Go
ld
e
n
s
tein
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Pric
e
,
an
d
th
e
p
er
s
o
n
alize
d
Fin
d
Valu
es
f
u
n
ctio
n
s
a
r
e
th
e
ch
o
s
en
o
n
e
f
o
r
v
alid
atio
n
.
T
ab
le
3
ex
p
o
s
es th
e
r
an
g
e
a
n
d
g
l
o
b
al
m
in
im
u
m
o
f
ea
ch
f
u
n
ctio
n
.
T
ab
le
3
.
Valid
atio
n
f
u
n
ctio
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s
d
etails
F
u
n
c
t
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R
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2
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F
i
n
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V
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l
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e
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=
[
100
700
50
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400
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=
[
0
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200
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(
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e
liter
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r
e,
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e
‘
Fin
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f
u
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n
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er
s
o
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u
n
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h
o
wn
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Fig
u
r
e
4
.
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t
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alu
es
s
elec
ted
b
y
th
e
alg
o
r
i
th
m
s
,
an
d
th
e
v
ec
to
r
to
b
e
f
o
u
n
d
in
t
h
is
ca
s
e
is
[
2
1
5
5
5
-
1
0
6
7
4
-
7
0
1
]
.
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h
e
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er
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r
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e
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e
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r
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e
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o
r
ith
m
s
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DE
,
an
d
PS
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f
o
r
th
e
Sp
h
er
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f
u
n
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n
is
p
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esen
ted
in
Fig
u
r
e
4
(
a)
,
wh
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b
o
t
h
DE
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d
PS
O
h
av
e
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n
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e
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b
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m
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r
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t
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e
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ir
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t
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0
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atio
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s
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d
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o
r
ith
m
at
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o
u
t
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0
.
Fig
u
r
e
4
(
b
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d
is
p
lay
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th
e
co
n
v
er
g
en
ce
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s
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g
Go
ld
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m
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ar
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e
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s
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r
eg
ar
d
i
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g
th
e
s
am
e
alg
o
r
ith
m
s
(
GW
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d
PS
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at
ab
o
u
t
1
0
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s
.
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h
ile
it
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o
k
ab
o
u
t
1
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0
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at
io
n
s
to
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ea
ch
g
o
o
d
r
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s
in
g
DE
alg
o
r
ith
m
.
T
h
e
f
in
al
f
u
n
ctio
n
,
Fin
d
Valu
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
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r
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ti
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o
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ith
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(
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n
a
Za
id
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4337
in
Fig
u
r
e
4
(
c)
s
h
o
ws
th
e
co
n
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er
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c
u
r
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e
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a
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g
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im
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m
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ze
r
o
.
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co
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g
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e
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ir
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en
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in
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o
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s
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ch
g
o
o
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r
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o
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e
s
im
u
la
tio
n
n
ea
r
to
1
,
0
0
0
iter
atio
n
s
.
(
a)
(
b
)
(
c)
Fig
u
r
e
4
.
C
o
n
v
er
g
e
n
ce
b
eh
a
v
i
o
r
o
f
m
etah
eu
r
is
tic
alg
o
r
it
h
m
s
o
n
d
if
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er
en
t
v
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atio
n
f
u
n
ctio
n
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(
a)
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h
er
e,
(
b
)
Go
ld
s
tein
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Pric
e,
a
n
d
(
c
)
Fin
d
Valu
es
T
h
e
th
r
ee
alg
o
r
ith
m
s
co
n
v
er
g
e
with
d
if
f
er
en
t
p
er
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o
r
m
an
ce
s
.
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r
ith
m
is
th
e
alg
o
r
ith
m
with
th
e
h
ig
h
er
n
u
m
b
er
o
f
iter
atio
n
s
n
e
ed
ed
to
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in
al
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n
v
er
g
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ce
in
th
e
Sp
h
er
e
a
n
d
G
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ld
s
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ice
f
u
n
ctio
n
s
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ile
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r
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ted
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etter
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er
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m
a
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ce
s
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m
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d
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e
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t
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er
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m
an
ce
s
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o
r
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th
r
ee
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u
n
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n
s
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All
th
e
th
r
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ith
m
s
s
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cc
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e
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o
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ly
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e
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etah
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r
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ith
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d
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er
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ar
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ich
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ate
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er
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r
o
f
th
e
m
ac
h
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n
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n
g
m
o
d
el
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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.
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s
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n
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s
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n
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r
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T
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le
4
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r
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ac
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h
y
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e
r
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ar
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d
m
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ar
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eter
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u
s
ed
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u
r
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g
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e
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ch
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h
ase.
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h
e
r
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e
s
h
o
wn
i
n
Fig
u
r
e
5
.
Fig
u
r
e
5
(
a)
p
r
esen
ts
th
e
co
n
v
er
g
en
ce
c
u
r
v
e
d
u
r
in
g
th
e
er
r
o
r
m
in
im
izatio
n
b
y
tu
n
in
g
KNN
u
s
in
g
m
etah
e
u
r
is
tics
.
GW
O
h
as
r
ea
ch
ed
th
e
b
est
s
o
lu
tio
n
d
u
r
in
g
th
e
1
0
0
iter
atio
n
s
,
at
a
b
o
u
t
0
.
1
2
2
,
f
o
llo
wed
b
y
PS
O
th
en
DE
alg
o
r
ith
m
in
th
e
f
in
al
p
o
s
itio
n
.
Fig
u
r
e
5
(
b
)
d
is
p
lay
s
th
e
tu
n
in
g
o
f
SVM
h
y
p
er
p
ar
am
eter
s
,
wh
e
r
e
id
e
n
tical
b
est
s
o
lu
tio
n
h
as
b
ee
n
f
o
u
n
d
,
u
s
in
g
PS
O
th
is
tim
e,
f
o
llo
wed
b
y
DE
alg
o
r
ith
m
with
a
2
nd
b
est s
o
lu
tio
n
,
th
en
GW
O
b
y
th
e
en
d
.
T
ab
le
4
.
Hy
p
er
p
ar
a
m
eter
s
r
an
g
es
an
d
m
etah
e
u
r
is
tics
p
ar
am
eter
s
A
l
g
o
r
i
t
h
ms
H
y
p
e
r
p
a
r
a
me
t
e
r
s
PSO
G
W
O
DE
K
N
N
N
e
i
g
h
b
o
r
s
=
[
1
:
2
0
]
n
P
o
p
=
0
;
w
=
1
;
w
d
a
m
p
=
0
.
7
;
c
1
=
2
;
c
2
=
2
a
=
2
;
w
o
l
v
e
sN
o
=
2
0
n
P
o
p
=
2
0
;
b
e
t
a
_
m
i
n
=
0
.
2
;
b
e
t
a
_
ma
x
=
0
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9
;
p
C
R
=
0
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1
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M
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r
n
e
l
=
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C
=
[
0
.
1
:
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0
]
g
a
mm
a
=
[
0
.
0
1
:
1
5
]
(
a)
(
b
)
Fig
u
r
e
5
.
E
r
r
o
r
s
co
n
v
er
g
en
ce
cu
r
v
es
u
s
in
g
m
eta
h
eu
r
is
tics
alg
o
r
ith
m
s
f
o
r
(
a)
KNN
an
d
(
b
)
SVM
T
ab
le
5
r
esu
m
e
th
e
f
in
al
r
esu
lts
o
f
m
etah
eu
r
is
tics
alg
o
r
ith
m
s
f
o
r
ea
c
h
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el.
T
h
e
y
ex
p
o
s
e
th
e
b
est
f
o
u
n
d
s
o
lu
tio
n
u
s
in
g
cr
o
s
s
v
alid
atio
n
with
its
co
r
r
esp
o
n
d
in
g
h
y
p
er
p
ar
am
eter
s
co
m
b
in
atio
n
.
T
h
e
cr
o
s
s
-
v
alid
atio
n
tech
n
i
q
u
e
s
p
lits
to
tr
ain
in
g
an
d
test
in
g
d
ata
f
o
ld
s
r
a
n
d
o
m
l
y
,
it
is
u
s
ed
to
g
iv
e
m
o
r
e
r
ea
lis
tic
id
ea
o
f
h
o
w
well
th
e
m
o
d
el
g
e
n
er
alize
s
to
n
ew
in
f
o
r
m
atio
n
.
T
ab
le
5
.
KNN
an
d
SVM
r
esu
lts
b
y
o
p
tim
izatio
n
al
g
o
r
ith
m
s
A
l
g
o
r
i
t
h
ms
K
N
N
r
e
s
u
l
t
s
S
V
M
r
e
s
u
l
t
s
Er
r
o
r
N
_
N
e
i
g
h
b
o
r
s
Er
r
o
r
C
G
a
mm
a
PSO
0
.
1
2
8
7
7
0
.
1
2
2
1
2
2
.
9
6
9
.
1
6
G
W
O
0
.
1
2
2
1
5
0
.
1
2
8
7
2
0
.
2
8
3
.
5
8
DE
0
.
1
3
2
0
3
0
.
1
2
5
4
8
.
0
0
6
8
.
1
9
Fig
u
r
e
6
is
th
e
b
o
x
p
lo
ts
f
o
r
th
e
s
o
lu
tio
n
s
f
o
u
n
d
b
y
ea
ch
m
etah
eu
r
is
tic
alg
o
r
ith
m
in
ea
ch
o
f
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
.
T
h
e
B
o
x
p
lo
t
is
a
r
o
b
u
s
tn
ess
test
th
at
p
r
o
v
id
es
an
in
f
o
r
m
ati
o
n
ab
o
u
t
s
o
lu
tio
n
s
d
is
p
er
s
io
n
b
y
r
u
n
n
i
n
g
th
e
alg
o
r
ith
m
s
f
o
r
1
0
r
u
n
tim
es
(
1
0
0
iter
atio
n
s
ea
ch
)
.
A
lar
g
er
b
o
x
in
d
icate
s
a
wid
er
s
p
r
ea
d
o
f
s
o
lu
tio
n
s
,
wh
ile
a
s
h
o
r
ter
b
o
x
in
d
icate
s
a
m
o
r
e
co
n
ce
n
tr
ated
s
et
o
f
s
o
lu
tio
n
s
.
Fig
u
r
e
6
(
a)
is
th
e
d
is
p
er
s
io
n
o
f
s
o
lu
tio
n
s
f
o
r
th
e
KNN
m
o
d
el,
it sh
o
ws
a
g
o
o
d
s
p
r
ea
d
o
v
er
th
e
1
0
r
u
n
s
,
with
o
n
ly
o
n
e
o
u
tlier
p
er
alg
o
r
ith
m
,
with
a
m
in
im
u
m
er
r
o
r
f
o
u
n
d
o
f
0
.
1
1
8
8
an
d
a
m
ax
im
u
m
o
f
ab
o
u
t
0
.
1
4
2
.
R
eg
ar
d
in
g
SVM
in
Fig
u
r
e
6
(
b
)
,
3
o
u
tlier
s
ar
e
d
et
ec
ted
o
n
th
e
PS
O
alg
o
r
ith
m
,
with
a
co
n
s
is
ten
cy
o
f
f
o
u
n
d
s
o
lu
tio
n
s
f
o
r
m
a
n
y
r
u
n
s
.
T
h
e
m
a
x
im
u
m
er
r
o
r
o
v
er
th
e
t
h
r
ee
al
g
o
r
ith
m
s
is
0
.
1
3
2
,
an
d
a
m
in
im
u
m
o
f
0
.
1
2
2
,
with
r
ea
s
o
n
ab
le
s
p
r
ea
d
s
.
Fro
m
th
e
co
n
v
er
g
en
ce
cu
r
v
es
in
Fig
u
r
e
5
,
we
o
b
s
er
v
e
th
a
t
we
co
u
ld
r
ea
c
h
er
r
o
r
s
o
f
a
b
o
u
t
0
.
1
2
,
wh
ich
is
g
r
ea
t
r
esu
lt
f
o
r
1
0
0
i
ter
atio
n
s
an
d
2
0
n
u
m
b
er
o
f
p
o
p
u
latio
n
s
.
O
n
th
e
o
th
e
r
h
a
n
d
,
th
e
b
o
x
p
lo
ts
s
h
o
w
th
e
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n
s
is
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ce
o
f
p
r
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v
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in
g
g
o
o
d
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[
1
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.
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.
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h
a
t
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,
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.
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2
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P
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.
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sam
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3
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R
.
C
.
W
o
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d
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u
f
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t
a
l
.
,
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4
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A
.
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5
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mm
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[
8
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L.
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.
-
A
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n
d
d
a
ta
sc
ien
c
e
tec
h
n
iq
u
e
s
fo
r
m
e
d
ica
l
a
p
p
li
c
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
z
a
id
.
n
o
u
n
a
-
e
t
u
@e
tu
.
u
n
iv
h
2
c
.
m
a
.
H
a
m
id
Bo
u
y
g
h
f
wa
s
b
o
rn
i
n
Err
a
c
h
id
ia,
M
o
r
o
c
c
o
,
in
1
9
8
2
.
H
e
g
o
t
h
is
B.
S
.
a
n
d
M
.
S
.
d
e
g
re
e
s
i
n
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
a
n
d
Tele
c
o
m
fro
m
th
e
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
i
n
F
e
z
,
M
o
r
o
c
c
o
,
in
2
0
0
7
,
a
n
d
h
is
P
h
.
D.
i
n
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
Tele
c
o
m
fro
m
Ha
ss
a
n
II
Un
i
v
e
rsity
o
f
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
,
in
2
0
1
9
.
F
ro
m
2
0
1
5
u
n
ti
l
2
0
1
9
,
h
e
wo
rk
e
d
a
s
a
Re
se
a
rc
h
As
sista
n
t
a
t
th
e
P
ri
n
c
e
to
n
P
las
m
a
P
h
y
sic
s
Lab
o
ra
to
r
y
.
S
in
c
e
2
0
1
9
,
h
e
h
a
s
wo
rk
e
d
a
s
a
n
As
sista
n
t
P
r
o
f
e
ss
o
r
a
t
t
h
e
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
De
p
a
rtme
n
t
o
f
Ha
ss
a
n
II
Un
iv
e
rsity
’s
F
S
T
M
o
h
a
m
m
e
d
ia
in
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
,
a
n
d
h
a
s
re
c
e
iv
e
d
h
is
h
a
b
il
it
a
ti
o
n
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
Artifi
c
ial
In
telli
g
e
n
c
e
in
2
0
2
3
.
I
n
th
e
t
o
p
ic
o
f
IC
o
p
ti
m
iza
ti
o
n
,
h
e
h
a
s
writt
e
n
n
u
m
e
ro
u
s
a
rti
c
les
.
His
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
b
i
o
m
e
d
ica
l
e
lec
tro
n
ics
,
a
n
a
lo
g
IC
d
e
sig
n
,
e
lec
tro
m
a
g
n
e
ti
c
field
s,
l
o
w
p
o
we
r
d
e
sig
n
,
a
n
d
BL
E
a
p
p
li
c
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
a
m
id
.
b
o
u
y
g
h
f@g
m
a
il
.
c
o
m
.
Mo
h
a
m
m
e
d
Na
h
id
stu
d
ie
d
e
lec
tro
n
ics
a
n
d
re
c
e
iv
e
d
t
h
e
B.
S
d
ip
lo
m
a
in
El
e
c
tro
n
ics
a
t
ENS
ET
I
n
stit
u
te,
M
o
h
a
m
m
e
d
ia,
M
o
ro
c
c
o
i
n
1
9
9
4
,
re
c
e
iv
e
d
th
e
Ag
g
re
g
a
ti
o
n
d
ip
l
o
m
a
o
f
e
lec
tro
n
ics
a
n
d
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
,
fro
m
ENS
ET
i
n
s
ti
tu
te
o
f
Ra
b
a
t,
M
o
ro
c
c
o
in
2
0
0
0
.
He
re
c
e
iv
e
d
th
e
DES
A
d
ip
lo
m
a
in
c
o
m
p
u
ter
e
n
g
in
e
e
ri
n
g
,
tele
c
o
m
s
a
n
d
m
u
lt
ime
d
ia
i
n
2
0
0
4
.
He
c
o
n
d
u
c
te
d
d
o
c
t
o
ra
l
re
se
a
rc
h
a
t
ima
g
e
s,
v
id
e
o
s
c
o
d
in
g
a
n
d
q
u
a
li
ty
a
ss
e
ss
m
e
n
t
u
n
d
e
r
p
sy
c
h
o
v
isu
a
l
q
u
a
li
ty
c
rit
e
ria
sin
c
e
2
0
0
4
a
t
M
o
h
a
m
e
d
V
u
n
iv
e
rsit
y
o
f
Ra
b
a
t,
M
o
ro
c
c
o
,
w
h
e
re
h
e
re
c
e
iv
e
d
h
is
P
h
.
D.
in
2
0
1
0
.
Dr.
Na
h
id
,
wh
ich
is
re
se
a
rc
h
p
r
o
fe
ss
o
r
i
n
t
h
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
o
f
M
o
h
a
m
m
e
d
ia
sin
c
e
2
0
1
1
,
c
o
n
d
u
c
ts
re
se
a
rc
h
o
n
v
isu
a
l
p
e
rc
e
p
ti
o
n
a
n
d
it
s
a
p
p
l
ica
ti
o
n
t
o
c
o
d
in
g
,
u
n
d
e
rsta
n
d
i
n
g
,
a
n
d
d
is
p
lay
o
f
v
isu
a
l
in
fo
rm
a
ti
o
n
a
n
d
to
h
u
m
a
n
v
isio
n
,
ima
g
e
q
u
a
li
t
y
,
a
n
d
d
ig
i
tal
ima
g
in
g
.
Re
se
a
rc
h
a
n
d
p
r
o
jec
ts
a
re
a
lso
fo
c
u
se
d
o
n
a
rti
ficia
l
in
telli
g
e
n
c
y
,
e
m
b
e
d
d
e
d
sy
ste
m
s,
b
i
o
m
e
d
ica
l
sy
ste
m
s,
sm
a
rt
g
ri
d
s,
d
ig
i
tal
a
n
d
a
n
a
l
o
g
IC
d
e
sig
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
h
a
m
m
e
d
.
n
a
h
i
d
@g
m
a
il
.
c
o
m
.
Issa
S
a
b
iri
wa
s
b
o
r
n
i
n
M
se
m
rir
,
M
o
ro
c
c
o
,
1
9
9
5
.
I
n
2
0
2
3
,
h
e
g
o
t
a
P
h
.
D.
d
e
g
re
e
in
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
a
n
d
A
rti
ficia
l
In
tell
ig
e
n
c
e
fr
o
m
ENS
E
T
M
o
h
a
m
m
e
d
ia
-
F
a
c
u
lt
y
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
i
q
u
e
Ha
ss
a
n
I
I
Un
i
v
e
rsity
o
f
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
.
His
li
c
e
n
se
d
e
g
re
e
i
n
Bio
M
e
d
ica
l
In
str
u
m
e
n
tatio
n
a
n
d
M
a
in
ten
a
n
c
e
,
a
t
th
e
In
stit
u
te
o
f
He
a
lt
h
S
c
ien
c
e
s
S
e
tt
a
t
-
M
o
ro
c
c
o
(I
S
S
S
)
a
t
th
e
Un
iv
e
rsit
y
Ha
ss
a
n
I,
S
e
tt
a
t
-
M
o
r
o
c
c
o
.
He
a
l
so
g
o
t
a
M
a
ste
r’s
d
e
g
re
e
in
b
io
m
e
d
ica
l
e
n
g
in
e
e
ri
n
g
fr
o
m
F
S
T
S
e
tt
a
t
i
n
2
0
1
8
.
His
wo
r
k
,
st
u
d
i
e
s,
a
n
d
i
n
tere
sts
a
re
fo
c
u
se
d
o
n
t
h
e
d
e
v
e
lo
p
m
e
n
t,
d
e
si
g
n
,
a
n
d
o
p
ti
m
iza
ti
o
n
o
f
e
lec
tro
n
ic
sy
st
e
m
s
fo
r
b
io
m
e
d
ica
l
e
n
g
in
e
e
rin
g
a
n
d
h
e
a
lt
h
sc
ien
c
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
issa
.
sa
b
iri
@e
tu
.
fstm
.
a
c
.
m
a
.
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