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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
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15
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3
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r
a
d
i
o
l
o
g
is
t
m
u
s
t
d
i
a
g
n
o
s
e
t
h
e
b
r
a
i
n
t
u
m
o
r
a
s
e
a
r
l
y
as
p
o
s
s
i
b
l
e
,
a
n
d
t
h
e
n
c
h
e
c
k
a
g
a
i
n
t
o
s
ee
i
f
t
h
e
r
es
u
l
ts
a
r
e
c
o
r
r
e
c
t
[
9
]
.
I
n
t
h
i
s
w
a
y
,
t
h
e
p
r
o
b
l
e
m
w
i
t
h
t
h
i
s
r
e
s
ea
r
c
h
i
s
d
u
e
t
o
t
h
e
l
ac
k
o
f
e
a
r
l
y
d
e
t
e
c
t
i
o
n
o
f
b
r
a
i
n
t
u
m
o
r
s
,
s
o
m
a
n
y
p
a
t
i
e
n
t
s
o
n
l
y
f
i
n
d
o
u
t
t
h
at
t
h
e
y
a
c
t
u
a
l
l
y
s
u
f
f
e
r
f
r
o
m
a
b
r
a
i
n
t
u
m
o
r
.
S
o
,
i
t
i
s
n
e
ce
s
s
a
r
y
to
c
a
r
r
y
o
u
t
r
e
s
e
a
r
c
h
u
s
i
n
g
M
L
t
e
c
h
n
i
q
u
e
s
.
M
L
is
a
s
u
b
s
e
t
o
f
a
r
t
i
f
ic
i
a
l
i
n
t
e
l
li
g
e
n
c
e
(
A
I
)
.
A
I
i
s
a
t
e
r
m
t
h
a
t
r
e
f
e
r
s
t
o
t
h
e
a
p
p
l
i
c
a
t
i
o
n
o
f
a
l
g
o
r
i
t
h
m
s
t
h
a
t
c
a
n
a
n
al
y
z
e
l
a
r
g
e
d
a
t
as
e
ts
t
o
c
l
as
s
i
f
y
,
r
e
c
o
m
m
e
n
d
,
p
r
e
d
i
c
t
,
a
n
d
o
t
h
e
r
s
.
U
n
d
e
r
t
h
e
A
I
is
M
L
.
M
L
i
s
t
h
e
p
r
o
c
es
s
o
f
l
e
a
r
n
i
n
g
m
o
d
e
l
s
u
s
i
n
g
r
e
a
l
d
a
t
a
t
o
c
at
e
g
o
r
i
z
e
,
p
r
e
d
i
c
t
,
a
n
d
d
e
t
e
c
t
b
as
e
d
o
n
t
r
a
i
n
i
n
g
o
b
s
e
r
v
a
t
i
o
n
s
m
a
d
e
b
y
h
u
m
a
n
s
.
T
h
e
r
e
s
u
l
t
s
w
i
l
l
a
p
p
l
y
t
o
f
u
t
u
r
e
d
a
t
a
[
1
0
]
.
T
h
e
r
e
a
r
e
m
a
n
y
m
e
t
h
o
d
s
i
n
M
L
,
i
n
c
l
u
d
i
n
g
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
k
-
n
e
ar
est
n
eig
h
b
o
r
(
KNN)
,
r
a
n
d
o
m
f
o
r
e
s
t
,
d
e
ci
s
i
o
n
t
r
e
e
s
(
D
T
s
)
,
a
n
d
m
u
l
t
i
-
l
a
y
e
r
p
e
r
c
e
p
t
r
o
n
(
M
L
P
)
[
1
1
]
.
A
p
a
r
t
f
r
o
m
t
h
a
t
,
i
n
M
L
t
h
e
r
e
a
r
e
s
e
v
e
r
a
l
o
t
h
e
r
m
e
t
h
o
d
s
t
h
a
t
c
a
n
b
e
u
s
e
d
,
n
a
m
e
l
y
l
o
g
is
ti
c
r
e
g
r
e
s
s
i
o
n
a
n
d
n
a
i
v
e
B
a
y
es
[
1
2
]
.
T
h
e
n
a
c
c
o
r
d
i
n
g
t
o
o
t
h
e
r
r
es
ea
r
c
h
,
X
GB
o
o
s
t
is
al
s
o
i
n
c
l
u
d
e
d
i
n
t
h
e
M
L
m
e
t
h
o
d
[
1
3
]
.
A
c
c
o
r
d
i
n
g
t
o
A
n
k
i
t
a
n
d
K
o
l
e
[
1
4
]
S
V
M
,
l
o
g
i
s
ti
c
r
e
g
r
es
s
i
o
n
,
K
N
N
,
n
a
i
v
e
B
a
y
es
,
r
a
n
d
o
m
f
o
r
e
s
t
c
l
a
s
s
i
f
i
e
r
,
a
n
d
X
G
B
o
o
s
t
c
la
s
s
i
f
i
e
r
a
r
e
m
e
th
o
d
s
t
h
a
t
c
a
n
b
e
u
s
e
d
t
o
d
e
t
e
c
t
b
r
a
i
n
t
u
m
o
r
s
.
Naiv
e
B
ay
es
i
s
an
ef
f
icien
t
an
d
ef
f
ec
tiv
e
alg
o
r
ith
m
in
ML
an
d
d
ata
m
in
in
g
.
Naiv
e
B
ay
es
wa
s
p
r
o
p
o
s
ed
b
y
T
h
o
m
as
B
ay
es
f
r
o
m
E
n
g
lan
d
.
Naiv
e
B
ay
es
is
p
r
o
v
en
to
b
e
f
ast
an
d
ac
c
u
r
a
te.
B
u
t
s
o
m
etim
es
n
aiv
e
B
ay
es
r
eq
u
ir
es
tr
ain
in
g
in
f
o
r
m
atio
n
in
th
e
f
o
r
m
o
f
m
e
an
an
d
v
ar
ia
n
ce
to
ca
r
r
y
o
u
t
c
lass
if
icatio
n
.
T
h
en
KNN.
KNN
is
an
al
g
o
r
ith
m
f
o
r
class
if
icatio
n
[
1
5
]
.
T
h
e
n
th
e
r
e
is
SVM
wh
ich
is
a
tec
h
n
iq
u
e
in
ML
.
SVM
is
a
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
el
with
ass
o
ciate
d
lear
n
in
g
alg
o
r
ith
m
s
.
SVM
aim
s
to
an
aly
ze
d
ata
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
.
Ad
d
itio
n
ally
,
SVM
ca
n
also
p
er
f
o
r
m
n
o
n
-
lin
ea
r
class
if
icatio
n
u
s
in
g
a
k
er
n
e
l
tr
ick
th
at
im
p
licitly
in
s
er
ts
it
in
to
a
h
ig
h
-
d
im
en
s
io
n
al
f
ea
t
u
r
e
s
p
ac
e
[
1
6
]
.
T
h
e
n
an
o
th
er
ML
tech
n
iq
u
e
is
r
an
d
o
m
f
o
r
est.
R
an
d
o
m
f
o
r
est
is
th
e
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
ML
alg
o
r
ith
m
d
u
e
t
o
its
s
im
p
licity
an
d
v
a
r
iety
.
T
h
ey
ar
e
u
s
u
ally
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
R
an
d
o
m
f
o
r
est is
an
ML
ap
p
r
o
ac
h
th
at
co
llects r
e
s
u
lts
f
r
o
m
a
s
in
g
le
d
ec
is
io
n
tr
ee
s
o
th
at
it
ca
n
in
cr
ea
s
e
p
r
e
d
ictiv
e
ef
f
icien
cy
th
r
o
u
g
h
v
o
tin
g
[
1
7
]
.
Ap
ar
t
f
r
o
m
n
aiv
e
B
ay
es
,
KNN,
SVM,
an
d
r
an
d
o
m
f
o
r
est,
we
also
u
s
e
lo
g
is
tic
r
eg
r
ess
io
n
an
d
XGBo
o
s
t
m
eth
o
d
s
.
L
o
g
is
tic
r
eg
r
ess
io
n
is
u
s
ed
to
m
o
d
el
b
in
ar
y
o
u
tco
m
es.
L
o
g
is
tic
r
e
g
r
ess
io
n
in
v
o
lv
es
esti
m
atio
n
task
s
.
T
h
e
r
esp
o
n
s
e
o
n
ly
h
as
two
p
o
s
s
ib
le
o
u
tco
m
es,
n
am
ely
th
at
it
ca
n
b
e
r
ep
r
esen
ted
b
y
a
b
in
ar
y
v
ar
iab
le
wi
th
th
e
v
alu
es
0
an
d
1
.
Fo
r
XGBo
o
s
t,
b
o
o
s
tin
g
i
n
v
o
lv
es
co
llectin
g
all
wea
k
cl
ass
if
icatio
n
s
to
p
r
o
d
u
ce
a
s
tr
o
n
g
class
if
icatio
n
.
Usu
ally
wh
at
is
d
o
n
e
in
th
e
f
i
r
s
t
s
tep
is
to
o
r
g
an
ize
th
e
d
ata
.
All
f
o
r
m
s
o
f
d
ata
will
b
e
c
o
n
v
er
ted
to
n
u
m
er
ic
b
ec
au
s
e
XGBo
o
s
t
o
n
ly
wo
r
k
s
with
v
ec
to
r
n
u
m
er
ic.
T
h
en
c
lean
th
e
d
ata
an
d
r
u
n
th
r
o
u
g
h
th
e
f
ea
tu
r
es
[
1
8
]
.
B
ased
o
n
th
e
b
ac
k
g
r
o
u
n
d
th
at
h
as
b
ee
n
e
x
p
lain
ed
,
th
is
r
esear
ch
will
f
o
cu
s
o
n
ap
p
ly
in
g
ML
tech
n
iq
u
es
u
s
in
g
XGBo
o
s
t,
lo
g
is
tic
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
KNN,
n
aiv
e
B
ay
es
,
an
d
SVM
to
d
etec
t
b
r
ain
tu
m
o
r
s
u
s
in
g
f
ea
tu
r
es
as
cr
iter
ia.
E
ac
h
p
iec
e
o
f
d
ata
in
t
h
is
r
esear
ch
will
b
e
d
i
v
id
ed
i
n
to
tr
ain
i
n
g
a
n
d
test
in
g
d
ata.
Af
ter
th
at,
ea
ch
ML
m
eth
o
d
will b
e
u
s
ed
,
tr
ain
ed
a
n
d
e
v
alu
ated
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
I
n
th
is
s
ec
tio
n
,
r
esear
ch
er
s
u
s
e
liter
atu
r
e
s
tu
d
ies
in
in
ter
n
at
io
n
al
jo
u
r
n
als
o
r
co
n
f
er
en
ce
s
r
elate
d
t
o
th
e
r
esear
ch
ed
to
p
ic.
I
n
th
is
ch
ap
ter
,
th
e
r
esear
ch
er
aim
s
to
s
tu
d
y
an
d
also
lo
o
k
f
o
r
v
ar
io
u
s
r
ef
er
e
n
ce
s
r
eg
ar
d
in
g
b
r
ain
tu
m
o
r
d
etec
tio
n
,
ML
tech
n
iq
u
es,
an
d
o
th
er
s
to
s
ee
th
e
p
r
o
b
lem
s
th
at
ex
is
t
t
o
d
ay
an
d
also
f
in
d
s
o
lu
tio
n
s
to
th
ese
p
r
o
b
lem
s
.
T
h
e
r
esu
lts
o
f
th
e
liter
atu
r
e
s
tu
d
y
r
eg
ar
d
i
n
g
b
r
ai
n
tu
m
o
r
d
etec
t
io
n
ca
n
b
e
s
ee
n
in
T
ab
le
1
[
1
9
]
–
[
2
5
]
(
s
ee
in
Ap
p
en
d
ix
)
an
d
th
e
r
esu
lts
o
f
th
e
liter
atu
r
e
s
tu
d
y
r
eg
a
r
d
in
g
ML
tech
n
iq
u
es
ca
n
b
e
s
ee
n
in
T
ab
le
2
[
2
6
]
–
[
3
1
]
(
s
ee
in
Ap
p
en
d
i
x
)
.
3.
M
E
T
H
O
D
I
n
th
is
ch
ap
ter
,
r
esear
ch
er
s
will
ex
p
lain
wh
at
ML
tech
n
i
q
u
es
ar
e
u
s
ed
in
r
esear
ch
.
T
h
er
e
ar
e
6
,
n
am
ely
XGBo
o
s
t,
lo
g
is
tic
r
e
g
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
KNN,
n
aiv
e
B
ay
es
,
an
d
SVM.
T
h
e
ex
p
lan
atio
n
will
s
tar
t in
s
ec
tio
n
s
3
.
1
to
3
.
6
.
3
.
1
.
XG
B
o
o
s
t
XGBo
o
s
t
was
f
ir
s
t
p
r
o
p
o
s
ed
b
y
C
h
en
an
d
G
u
estrin
in
2
0
1
6
[
3
2
]
.
XGBo
o
s
t
is
a
tr
ee
-
b
as
ed
m
eth
o
d
th
at
is
p
o
p
u
lar
in
ML
.
Mo
s
tly
XGBo
o
s
t
i
s
u
s
ed
as
a
b
ase
m
o
d
el
to
s
o
lv
e
ML
task
s
.
XGB
o
o
s
t
is
a
m
ix
tu
r
e
o
f
b
ag
g
in
g
an
d
b
o
o
s
tin
g
alg
o
r
ith
m
s
th
at
ca
n
im
p
r
o
v
e
ac
cu
r
ac
y
s
eq
u
en
tially
[
3
3
]
.
XGb
o
o
s
t
ca
n
id
en
tify
ef
f
ec
tiv
e
an
d
ef
f
icien
t
way
s
to
co
m
b
in
e
lo
ca
l
an
d
g
lo
b
al
co
n
tex
tu
al
p
atter
n
s
.
XGBo
o
s
t
is
s
tr
o
n
g
ag
ain
s
t
o
v
er
f
itti
n
g
wh
ich
m
ak
es
it
v
er
y
ea
s
y
f
o
r
t
h
e
m
o
d
el
to
m
a
k
e
ch
o
ices.
B
y
u
s
in
g
XGBo
o
s
t,
r
esear
ch
er
s
ca
n
id
en
tify
s
tr
o
n
g
p
atter
n
s
an
d
b
e
ab
le
to
d
if
f
er
e
n
tiate
b
etwe
en
p
atien
ts
an
d
h
e
alth
y
p
eo
p
le
[
3
4
]
.
T
h
is
is
in
lin
e
with
o
th
er
r
esea
r
ch
th
at
th
e
XGBo
o
s
t
m
o
d
el
p
er
f
o
r
m
s
class
if
icatio
n
v
er
y
we
ll
an
d
h
as
th
e
s
am
e
p
r
o
p
o
r
tio
n
s
f
o
r
b
o
th
class
es.
Acc
o
r
d
in
g
t
o
o
th
e
r
r
esear
ch
,
XGBo
o
s
t
is
a
b
etter
m
o
d
el
th
a
n
s
in
g
l
e
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
-
8
7
0
8
I
mp
leme
n
tin
g
b
r
a
in
t
u
mo
r
d
et
ec
tio
n
u
s
in
g
va
r
io
u
s
ma
ch
in
e
l
ea
r
n
in
g
tech
n
i
q
u
es
(
R
a
n
i P
u
s
p
ita
)
3311
ML
m
o
d
els
s
u
ch
as
lo
g
is
tic
r
e
g
r
ess
io
n
,
SVM,
d
ec
is
io
n
tr
ee
s
,
an
d
o
th
er
s
.
C
r
o
s
s
-
v
alid
atio
n
b
etwe
en
m
o
d
els
is
p
o
wer
f
u
l
an
d
in
teg
r
ated
in
XGBo
o
s
t.
Acc
o
r
d
in
g
to
th
is
r
esear
ch
,
XGBo
o
s
t
is
a
m
o
d
el
th
at
is
b
u
ilt
s
eq
u
en
tially
to
r
ed
u
ce
ac
cu
r
ac
y
in
iter
atio
n
s
.
Gr
ad
ien
t
b
o
o
s
t
in
g
is
th
e
o
r
ig
in
al
m
o
d
el
o
f
X
GB
o
o
s
t.
W
h
er
e
th
e
way
it wo
r
k
s
is
to
co
m
b
i
n
e
th
e
wea
k
s
o
th
at
th
ey
b
ec
o
m
e
s
tr
o
n
g
er
.
3
.
2
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
L
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
els
t
h
e
r
elatio
n
s
h
ip
b
etwe
en
ca
te
g
o
r
ical
v
ar
iab
les
an
d
co
v
a
r
i
ates.
T
h
is
m
eth
o
d
c
o
m
b
in
es
lin
e
r
in
d
ep
en
d
en
t
v
ar
iab
les
with
lo
g
-
o
d
d
s
o
f
a
p
r
o
b
ab
ilit
y
i
n
a
l
o
g
is
tic
m
o
d
el.
L
o
g
is
tic
r
eg
r
ess
io
n
ca
n
b
e
co
n
s
id
er
ed
ML
[
3
5
]
.
L
o
g
is
tic
r
eg
r
ess
io
n
is
u
s
ed
to
an
aly
ze
d
ata
in
s
tati
s
tics
th
at
d
eter
m
in
e
th
e
r
elatio
n
s
h
ip
b
etwe
en
o
n
e
o
r
m
o
r
e
i
n
d
ep
e
n
d
en
t
v
a
r
iab
les
an
d
th
e
d
e
p
en
d
e
n
t
v
ar
iab
le.
T
h
is
m
eth
o
d
is
u
s
ed
f
o
r
p
r
ed
ictio
n
.
T
h
e
d
ataset
co
n
tain
s
th
e
tar
g
et
class
.
I
n
th
is
ca
s
e,
let'
s
s
ay
X
is
a
b
in
ar
y
r
esp
o
n
s
e
v
ar
ia
b
le.
=
1
if
p
r
esen
t,
an
d
=
0
if
ab
s
en
t,
th
en
th
e
d
ata
[
1
,
2
,
.
.
.
,
]
ar
e
in
d
ep
en
d
en
t.
L
et
πi
b
e
th
e
p
r
o
b
a
b
ilit
y
o
f
s
u
cc
ess
.
T
h
en
th
e
lo
g
is
tic
f
u
n
ctio
n
f
o
r
πi
is
:
(
)
=
1
−
0
+
1
1
+
2
2
+
·
·
·
+
,
;
(
1
)
wh
er
e
,
=
(
0
+
1
1
+
2
2
+
·
·
·
+
,
)
1
+
(
0
+
1
1
+
2
2
+
·
·
·
+
,
)
=
(
0
)
1
+
(
0
)
=
(
′
)
(
2
)
3
.
3
.
Ra
nd
o
m
f
o
re
s
t
R
an
d
o
m
f
o
r
est
is
a
p
o
p
u
lar
tech
n
iq
u
e
in
ML
.
R
an
d
o
m
f
o
r
est
h
as
s
u
cc
ess
f
u
lly
d
em
o
n
s
tr
ated
th
e
s
u
cc
ess
o
f
its
m
eth
o
d
in
v
ar
io
u
s
f
ield
s
,
in
clu
d
in
g
th
e
h
ea
lth
s
ec
to
r
.
R
an
d
o
m
f
o
r
ests
h
av
e
th
e
ab
ilit
y
to
h
an
d
le
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata,
ca
p
tu
r
e
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
,
a
n
d
s
o
o
n
,
s
o
r
an
d
o
m
f
o
r
ests
ar
e
th
e
r
ig
h
t
c
h
o
ice
o
f
m
eth
o
d
f
o
r
p
r
ed
ictio
n
s
in
th
e
h
ea
lth
s
ec
to
r
[
3
6
]
.
T
h
e
r
an
d
o
m
f
o
r
est
m
eth
o
d
was
s
u
g
g
e
s
ted
b
y
B
r
eim
an
in
2
0
0
1
.
R
an
d
o
m
f
o
r
est
p
r
o
d
u
c
es
two
ty
p
es
o
f
in
f
o
r
m
atio
n
s
ig
n
if
ican
ce
,
n
am
ely
m
ea
s
u
r
i
n
g
th
e
s
ig
n
if
ica
n
ce
in
clu
d
in
g
th
e
r
esp
o
n
s
e
v
ar
iab
l
e,
an
d
m
ea
s
u
r
in
g
th
e
in
ter
n
al
d
ata
m
o
d
el
[
3
7
]
.
T
h
er
e
ar
e
s
ev
er
al
p
h
ases
o
f
th
e
r
an
d
o
m
f
o
r
est alg
o
r
ith
m
,
n
am
ely
:
a.
Dete
r
m
in
e
a
tr
ain
e
d
m
o
d
el:
T
h
e
p
o
i
n
t
is
to
d
eter
m
in
e
t
h
e
tr
ain
in
g
m
o
d
el.
T
h
is
tech
n
iq
u
e
is
u
s
ed
in
co
llectin
g
class
if
icatio
n
m
o
d
el
s
f
r
o
m
o
r
i
g
in
al
d
ata.
b.
B
u
ilt r
an
d
o
m
f
o
r
est s
tr
u
ctu
r
e:
T
h
e
p
o
in
t is to
c
o
llect
d
ata
f
r
o
m
all
b
o
o
ts
tr
ap
s
an
d
p
r
o
d
u
ce
n
tr
ee
s
.
c.
Vo
tin
g
p
h
ase:
W
h
at
th
is
m
ea
n
s
is
th
e
v
o
tin
g
o
r
p
o
o
lin
g
p
h
ase.
W
h
er
e
th
is
p
h
ase
h
elp
s
u
s
d
eter
m
in
e
th
e
r
ig
h
t a
n
d
wr
o
n
g
f
ea
tu
r
es f
o
r
e
ac
h
tr
ee
.
T
h
is
is
in
lin
e
with
th
e
o
p
in
io
n
o
f
o
th
er
r
esear
ch
er
s
th
at
r
an
d
o
m
f
o
r
est
is
a
tr
ee
-
b
ased
ML
tech
n
iq
u
e.
R
an
d
o
m
f
o
r
est
esti
m
ates
co
r
r
ec
tly
an
d
t
h
e
r
elatio
n
s
h
ip
s
a
r
e
s
tab
le
b
etwe
en
v
a
r
iab
les
[
3
8
]
.
R
an
d
o
m
f
o
r
est
is
ab
le
to
h
an
d
le
n
u
m
er
ic
o
r
ca
teg
o
r
ical
v
ar
ia
b
les in
p
r
ed
ictio
n
s
itu
atio
n
s
.
3
.
4
.
K
NN
T
h
e
b
asic
ML
m
o
d
el
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
is
KN
N.
T
h
e
g
o
al
is
to
d
eter
m
in
e
th
e
d
is
tan
ce
b
etwe
en
n
ew
u
n
lab
eled
d
ata
p
o
in
ts
an
d
th
e
tr
ain
i
n
g
d
ata
s
et
s
to
r
ed
in
f
ea
tu
r
e
s
p
ac
e.
T
h
e
k
v
alu
e
in
KNN
is
a
h
y
p
er
p
ar
am
eter
u
s
ed
to
s
o
r
t
t
h
e
k
-
n
ea
r
est
d
ata
p
o
i
n
ts
.
I
n
th
e
o
p
tim
ized
KNN,
n
eig
h
b
o
r
s
o
n
ly
h
av
e
p
o
s
itiv
e
r
elatio
n
s
h
ip
s
with
r
e
q
u
esto
r
s
.
Nea
r
b
y
n
eig
h
b
o
r
s
ca
n
i
n
f
lu
en
ce
b
etter
th
an
d
is
tan
t
n
ei
g
h
b
o
r
s
.
T
h
e
KNN
alg
o
r
ith
m
u
s
es
a
d
is
tan
ce
f
u
n
ctio
n
to
d
eter
m
in
e
th
e
weig
h
t
o
f
th
e
in
f
lu
e
n
ce
o
f
clo
s
e
n
ei
g
h
b
o
r
s
.
E
u
clid
ea
n
d
is
tan
ce
,
Pear
s
o
n
co
r
r
elatio
n
,
Ma
n
h
attan
d
is
tan
ce
,
an
d
s
p
e
ar
m
an
co
r
r
elatio
n
is
a
d
is
tan
ce
f
u
n
ctio
n
u
s
ed
f
o
r
co
n
tin
u
o
u
s
v
ar
iab
les.
T
h
er
e
a
r
e
p
r
o
s
an
d
c
o
n
s
to
u
s
in
g
KN
N.
T
h
e
ad
v
an
ta
g
e
is
th
at
it
u
s
es
a
n
o
n
p
ar
am
etr
ic
ap
p
r
o
ac
h
,
is
ea
s
y
to
u
n
d
er
s
tan
d
an
d
ea
s
y
to
ap
p
ly
.
B
u
t
th
e
co
n
is
th
at
KNN
is
n
o
t
ex
p
licit
ly
tr
ain
ed
q
u
ick
ly
.
Fig
u
r
e
1
.
is
a
n
illu
s
tr
atio
n
o
f
h
o
w
KNN
ca
n
d
o
its
j
o
b
u
s
in
g
E
u
clid
ea
n
d
is
tan
ce
.
An
ex
a
m
p
le
o
f
a
k
v
alu
e
is
s
ix
d
ata
p
o
in
ts
.
T
h
e
n
ew
s
am
p
le
d
ata
p
o
in
ts
f
all
in
to
t
h
e
b
lu
e
class
in
th
is
illu
s
tr
atio
n
[
3
9
]
.
Fig
u
r
e
1
.
I
ll
u
s
tr
atio
n
u
s
in
g
K
NN
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
3
0
9
-
3
3
1
8
3312
3
.
5
.
Na
i
v
e
B
a
y
es
Naiv
e
B
ay
es
is
a
m
eth
o
d
u
s
e
d
f
o
r
p
r
o
b
a
b
ilis
tic
class
if
icat
i
o
n
wh
ich
was
d
ev
el
o
p
ed
b
ase
d
o
n
B
ay
es
th
eo
r
em
.
Naiv
e
B
ay
es
aim
s
t
o
im
p
r
o
v
e
th
e
lear
n
i
n
g
ef
f
ici
en
cy
o
f
class
if
icatio
n
task
s
.
Naiv
e
B
a
y
es
ca
n
b
e
tr
ain
ed
to
u
n
d
e
r
s
tan
d
p
ar
am
et
er
s
b
y
ex
tr
ac
tin
g
b
asic
s
tatis
ti
cs
p
er
class
f
r
o
m
f
ea
tu
r
es.
T
h
is
m
o
d
el
m
ay
n
o
t
d
ep
en
d
o
n
B
ay
esian
alg
o
r
ith
m
s
.
I
n
th
e
r
ea
l
wo
r
l
d
,
n
ai
v
e
B
a
y
e
s
ca
n
b
e
u
s
ed
f
o
r
r
ea
l
-
tim
e
p
r
ed
ictio
n
an
d
tex
t
class
if
icatio
n
.
T
h
e
wea
k
n
ess
o
f
n
aiv
e
B
ay
es
is
th
at
th
is
m
eth
o
d
is
s
im
p
le
an
d
th
is
m
et
h
o
d
o
n
ly
r
e
q
u
ir
es
a
lim
ited
am
o
u
n
t
o
f
d
ata
to
esti
m
ate
th
e
r
eq
u
ir
ed
ca
teg
o
r
izatio
n
.
T
h
en
th
is
m
eth
o
d
d
o
es
n
o
t
n
ee
d
to
ev
alu
ate
th
e
en
tire
co
v
a
r
ian
ce
m
atr
i
x
.
T
h
e
f
o
llo
win
g
is
th
e
f
o
r
m
u
la
i
n
n
aiv
e
B
ay
es
:
(
|
)
=
(
|
)
∗
(
)
(
)
(
3
)
3
.
6
.
SVM
SVM
is
a
s
u
p
er
v
is
ed
ML
m
eth
o
d
th
at
d
ev
elo
p
s
a
d
ec
is
io
n
b
o
u
n
d
ar
y
o
r
h
y
p
e
r
p
lan
e.
SV
M
allo
ws
p
r
ed
ictin
g
lab
els
b
ased
o
n
a
v
ec
to
r
'
s
s
in
g
le
o
r
m
u
ltip
le
f
ea
tu
r
es.
SVM
co
m
es
f
r
o
m
th
e
n
am
e
o
f
th
e
clo
s
est
p
o
in
ts
wh
ich
ar
e
k
n
o
wn
as
s
u
p
p
o
r
t
v
ec
to
r
s
.
B
ased
o
n
th
is
r
esear
ch
,
SVM
is
m
o
s
t
o
f
ten
u
s
ed
f
o
r
b
i
o
m
ed
ia
p
r
ac
tice
to
au
to
m
atica
lly
ca
te
g
o
r
ize
p
r
o
f
iles
.
Fo
r
ex
am
p
le
,
d
eo
x
y
r
ib
o
n
u
cleic
ac
id
(
DN
A
)
s
eq
u
en
ce
s
,
g
en
e
ex
p
r
ess
io
n
p
r
o
f
iles
,
an
d
s
o
o
n
.
All
o
f
th
is
ca
n
b
e
u
s
ed
with
th
e
SVM
m
eth
o
d
.
Fig
u
r
e
2
is
an
ex
am
p
le
o
f
s
ce
n
ar
io
class
if
icatio
n
u
s
in
g
SVM
tech
n
iq
u
es.
Fig
u
r
e
2
.
C
lass
if
icatio
n
u
s
in
g
SVM
tech
n
iq
u
es
4.
P
RO
P
O
SE
D
SO
L
UT
I
O
N
M
E
T
H
O
D
Pro
p
o
s
ed
m
eth
o
d
aim
s
to
f
in
d
s
o
lu
tio
n
s
to
r
esear
ch
p
r
o
b
lem
s
th
at
h
av
e
b
ee
n
ex
p
lain
ed
in
th
e
b
ac
k
g
r
o
u
n
d
.
T
h
is
r
esear
ch
m
eth
o
d
is
wr
itten
in
f
r
am
ewo
r
k
f
o
r
m
to
m
a
k
e
it
ea
s
ier
to
u
n
d
er
s
tan
d
s
o
as
to
ac
h
iev
e
th
e
g
o
als
in
a
s
tr
u
ctu
r
ed
m
an
n
er
.
T
h
e
r
esear
c
h
m
eth
o
d
o
l
o
g
y
f
o
r
s
o
lu
tio
n
s
b
ased
o
n
b
r
ain
er
r
o
r
d
etec
tio
n
will b
e
ex
p
lain
e
d
in
Fig
u
r
e
3
.
B
ased
o
n
th
e
f
r
am
ewo
r
k
in
Fig
u
r
e
3
,
th
e
f
ir
s
t
s
tag
e
in
r
esear
ch
is
d
ata
co
llectio
n
an
d
d
ata
ex
p
lo
r
atio
n
.
I
n
d
ata
ex
p
lo
r
ati
o
n
,
r
esear
ch
er
s
lo
o
k
f
o
r
a
n
y
class
e
s
in
th
e
d
ata,
th
en
ch
ec
k
f
o
r
n
u
lls
,
u
s
e
Min
Ma
x
Scaler
an
d
d
iv
id
e
th
e
d
ata
in
to
8
0
tr
ain
in
g
d
ata
an
d
2
0
test
in
g
d
ata.
Af
ter
t
h
at,
r
esear
ch
er
s
will
co
n
d
u
ct
ex
p
er
im
en
ts
u
s
in
g
s
ix
m
eth
o
d
s
,
n
am
el
y
XGBo
o
s
t,
lo
g
is
tic
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
KNN,
n
aiv
e
B
ay
es
,
an
d
SVM
an
d
o
b
tai
n
t
h
eir
ac
cu
r
ac
y
.
Af
ter
th
at,
th
e
r
esear
ch
er
ca
n
s
ee
th
e
r
esu
lts
r
ep
o
r
t
in
th
e
f
o
r
m
o
f
p
r
ec
is
io
n
,
r
ec
all,
an
d
F
1
-
s
co
r
e
f
o
r
ea
c
h
r
esear
ch
m
et
h
o
d
ca
r
r
ied
o
u
t.
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
-
8
7
0
8
I
mp
leme
n
tin
g
b
r
a
in
t
u
mo
r
d
et
ec
tio
n
u
s
in
g
va
r
io
u
s
ma
ch
in
e
l
ea
r
n
in
g
tech
n
i
q
u
es
(
R
a
n
i P
u
s
p
ita
)
3313
Fig
u
r
e
3
.
Pro
p
o
s
ed
m
et
h
o
d
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
th
e
r
esear
ch
er
d
iv
id
es
it
in
to
two
s
ec
tio
n
s
,
n
am
ely
s
ec
tio
n
s
5
.
1
an
d
5
.
2
.
I
n
s
ec
tio
n
5
.
1
,
th
e
r
esear
ch
er
ex
p
lain
s
an
d
d
is
cu
s
s
es
ab
o
u
t
d
ataset.
R
esear
ch
er
s
co
llect
d
ata
an
d
also
ex
p
lo
r
e
th
e
d
ata
th
at
h
as
b
ee
n
co
llected
.
An
d
i
n
s
ec
tio
n
5
.
2
,
th
e
r
esear
ch
er
e
x
p
lain
s
th
e
r
esu
lts
o
f
ex
p
e
r
im
en
ts
th
at
h
av
e
b
ee
n
ca
r
r
ied
o
u
t
u
s
in
g
s
ev
er
al
m
et
h
o
d
s
c
h
o
s
en
b
ased
o
n
a
liter
a
tu
r
e
r
e
v
iew.
T
h
e
m
et
h
o
d
s
a
r
e
XGBo
o
s
t,
lo
g
is
tic
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
KN
N,
n
aiv
e
B
ay
es
,
an
d
SVM.
5
.
1
.
Co
llect
ing
a
nd
ex
plo
ring
da
t
a
s
et
R
esear
ch
er
s
u
s
ed
d
ata
f
r
o
m
t
h
e
K
a
g
g
le.
co
m
s
ite.
T
h
is
d
ataset
is
ab
o
u
t
b
r
ain
tu
m
o
r
f
ea
tu
r
e
in
clu
d
in
g
f
iv
e
f
ir
s
t
o
r
d
er
f
ea
tu
r
es
a
n
d
eig
h
t
tex
tu
r
e
f
ea
tu
r
es
with
th
e
ta
r
g
et
lev
el.
T
h
er
e
wer
e
3
,
7
6
2
d
ata
in
th
is
r
esear
ch
.
T
h
er
e
is
n
o
d
u
p
licate
d
ata
s
o
th
e
d
ata
r
em
ain
s
3
,
7
6
2
an
d
is
n
o
t
r
ed
u
ce
d
.
Af
ter
co
llecti
n
g
th
e
d
ataset,
we
co
n
tin
u
e
with
d
ataset
e
x
p
lo
r
a
tio
n
.
R
esear
ch
er
s
ca
r
r
y
o
u
t
ex
p
lo
r
atio
n
b
y
lo
o
k
in
g
at
class
,
v
ar
ian
ce
,
s
tan
d
a
r
d
d
ev
iatio
n
,
e
n
tr
o
p
y
,
s
k
ewn
ess
,
k
u
r
to
s
is
,
co
n
tr
ast,
en
er
g
y
,
a
n
g
u
lar
s
ec
o
n
d
m
o
m
en
t
(
ASM
)
,
h
o
m
o
g
en
eity
,
an
d
d
is
s
im
ilar
ity
.
T
h
e
f
ir
s
t
o
r
d
er
f
ea
tu
r
es
ar
e
m
ea
n
,
v
ar
ian
ce
,
s
tan
d
ar
d
d
e
v
iatio
n
,
s
k
ewn
ess
,
an
d
k
u
r
to
s
is
.
T
h
en
th
e
s
ec
o
n
d
o
r
d
er
f
ea
tu
r
es
ar
e
C
o
n
tr
ast,
en
e
r
g
y
,
ASM,
e
n
tr
o
p
y
,
h
o
m
o
g
e
n
eity
,
d
is
s
im
ilar
ity
,
co
r
r
elatio
n
,
an
d
co
ar
s
en
ess
.
T
h
en
th
e
r
esear
c
h
er
s
also
ca
r
r
ied
o
u
t
th
e
Min
Ma
x
Scaler
b
ef
o
r
e
ca
r
r
y
in
g
o
u
t
ex
p
er
im
e
n
ts
an
d
ev
alu
atio
n
s
.
Min
Ma
x
Scaler
is
u
s
u
ally
u
s
ed
to
cr
ea
te
d
ata
in
th
e
r
a
n
g
e
0
-
1
.
T
h
e
class
co
lu
m
n
d
e
f
in
es
wh
eth
er
th
e
im
ag
e
h
as a
tu
m
o
r
o
r
n
o
t.
I
f
th
er
e
is
a
tu
m
o
r
,
it is
lab
eled
1
; if
n
o
t,
it is
lab
eled
0
.
5
.
2
.
T
he
re
s
ults
B
ased
o
n
th
e
s
tep
s
th
at
h
av
e
b
ee
n
ta
k
en
b
ef
o
r
e
g
ettin
g
r
esu
lts
s
u
ch
as
co
llectin
g
an
d
ex
p
lo
r
i
n
g
d
atasets
,
d
iv
id
in
g
d
atasets
,
a
n
d
o
th
er
s
,
in
th
is
ch
ap
ter
,
t
h
e
r
esear
ch
er
will
p
r
esen
t
th
e
r
esu
lts
o
f
th
eir
ex
p
er
im
en
ts
.
T
a
b
le
3
is
an
e
x
p
lan
atio
n
o
f
th
e
m
eth
o
d
u
s
ed
an
d
its
ac
cu
r
ac
y
.
T
h
en
th
er
e
i
s
also
a
co
n
f
u
s
io
n
m
atr
ix
o
b
tain
ed
f
r
o
m
th
e
s
ix
m
eth
o
d
s
in
T
ab
le
4
.
C
o
n
f
u
s
io
n
m
atr
ix
is
an
im
p
o
r
tan
t p
er
f
o
r
m
an
ce
ev
alu
atio
n
in
ar
tific
ial
in
tellig
en
ce
th
at
p
r
o
v
id
es
an
o
v
er
all
p
ictu
r
e
o
f
t
h
e
p
r
e
d
ictio
n
r
esu
lts
o
f
th
e
m
o
d
el
u
s
ed
.
T
h
en
,
Fig
u
r
e
4
is
a
co
m
p
ar
is
o
n
o
f
s
ev
er
al
m
eth
o
d
s
u
s
ed
in
th
is
r
es
ea
r
ch
ex
p
e
r
im
en
t.
B
ased
o
n
th
e
s
ix
m
eth
o
d
s
u
s
ed
,
in
clu
d
in
g
XGBo
o
s
t,
lo
g
is
ti
c
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
KNN,
n
aiv
e
B
ay
es,
an
d
SVM,
r
an
d
o
m
f
o
r
est
is
th
e
m
eth
o
d
th
at
p
r
o
d
u
ce
s
th
e
h
ig
h
est
ac
cu
r
ac
y
,
n
am
el
y
9
8
.
4
1
%.
XGBo
o
s
t
g
o
t
an
ac
cu
r
ac
y
o
f
9
8
.
1
4
%,
l
o
g
is
tic
r
eg
r
ess
io
n
g
o
t
a
n
ac
c
u
r
a
cy
o
f
9
7
.
3
4
%,
KNN
an
d
n
ai
v
e
B
ay
es
o
f
9
7
.
3
4
%,
an
d
SVM
o
f
9
7
.
8
8
%.
All
m
o
d
els
ac
h
iev
ed
g
o
o
d
ac
cu
r
ac
y
,
b
u
t
wh
en
co
m
p
a
r
ed
,
r
a
n
d
o
m
f
o
r
est
is
th
e
b
est
m
eth
o
d
f
o
r
th
is
r
esear
ch
ca
s
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
3
0
9
-
3
3
1
8
3314
T
ab
le
3
.
E
v
alu
atio
n
r
esu
lt f
o
r
t
h
e
r
esear
ch
u
s
in
g
v
ar
io
u
s
ML
tech
n
iq
u
es
M
e
t
h
o
d
s
A
c
c
u
r
a
t
i
o
n
s
X
G
B
o
o
st
9
8
.
1
4
%
Lo
g
i
s
t
i
c
r
e
g
r
e
ssi
o
n
R
a
n
d
o
m
f
o
r
e
st
K
N
N
N
a
ï
v
e
B
a
y
e
s
S
V
M
9
7
.
3
4
%
9
8
.
4
1
%
9
7
.
3
4
%
9
7
.
3
4
%
9
7
.
8
8
%
T
ab
le
4
.
C
o
n
f
u
s
io
n
m
atr
ix
o
f
t
h
e
m
o
d
els
M
e
t
h
o
d
s
I
mag
e
s
M
e
t
h
o
d
s
I
mag
e
s
X
G
B
o
o
st
K
N
N
Lo
g
i
s
t
i
c
r
e
g
r
e
ssi
o
n
N
a
ï
v
e
B
a
y
e
s
R
a
n
d
o
m
f
o
r
e
st
S
V
M
Fig
u
r
e
4
.
I
m
ag
e
c
o
m
p
ar
is
o
n
o
f
all
ac
cu
r
ac
y
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3315
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RE
F
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NC
E
S
[
1
]
J.
A
m
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n
,
M
.
S
h
a
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f
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M
.
R
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Pr
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2
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3
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15
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No
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3
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3316
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[
5
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A
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Evaluation Warning : The document was created with Spire.PDF for Python.