I
nte
rna
t
io
na
l J
o
urna
l o
f
I
nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
,
p
p
.
1
0
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v15
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1078
J
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na
l ho
m
ep
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e
:
h
ttp
:
//ij
ict.
ia
esco
r
e.
co
m
Integ
ra
ting F
-
fil
t
er
a
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K
-
M
ea
ns
SM
O
TE
t
o
enha
n
ce
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ba
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Ris
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ticle
his
to
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R
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J
u
l 1
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5
R
ev
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Ma
y
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J
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l 5
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c
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s.
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p
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ft
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a
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E
m
p
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tu
r
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v
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F f
ilter
m
eth
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d
K
-
Me
an
s
SMOT
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L
ai’
s
g
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er
alize
d
weig
h
te
d
Up
lift
m
eth
o
d
Ma
ch
in
e
lear
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in
g
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
:
Sig
it Pr
iy
an
ta
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
s
an
d
E
lectr
o
n
ics,
Facu
lty
o
f
Ma
th
em
atics a
n
d
Natu
r
al
Scien
ce
s
Gad
jah
Ma
d
a
Un
iv
er
s
ity
B
u
lak
s
u
m
u
r
,
Kec
.
Dep
o
k
,
Ka
b
u
p
aten
Slem
an
,
5
5
2
8
1
,
Dae
r
ah
I
s
tim
ewa
Yo
g
y
ak
a
r
ta
E
m
ail: seag
atejo
g
ja@
u
g
m
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
An
ef
f
ec
tiv
e
o
r
g
an
izatio
n
u
n
d
er
s
tan
d
s
th
at
h
u
m
an
r
eso
u
r
c
es
ar
e
a
k
ey
d
r
iv
e
r
o
f
s
u
s
tain
ab
ilit
y
an
d
b
u
s
in
ess
g
r
o
wth
[
1
]
.
I
n
p
r
a
ctice
,
em
p
lo
y
ee
tu
r
n
o
v
er
,
v
o
lu
n
tar
y
s
ep
ar
atio
n
b
etwe
en
em
p
lo
y
ee
s
an
d
th
e
o
r
g
an
izatio
n
[
2
]
,
r
em
ain
s
a
co
m
m
o
n
c
h
allen
g
e
a
n
d
ca
n
in
cr
ea
s
e
r
ec
r
u
itm
en
t
co
s
ts
,
r
ed
u
ce
in
s
titu
tio
n
al
k
n
o
wled
g
e,
a
n
d
d
is
r
u
p
t
team
d
y
n
am
ics.
T
o
an
ticip
ate
tu
r
n
o
v
er
,
r
esear
ch
b
y
[
3
]
em
p
h
asizes
th
e
v
alu
e
o
f
HR
an
aly
tics
f
o
r
s
tr
ateg
ic
HR
p
la
n
n
in
g
.
On
e
o
f
th
e
m
o
s
t
ef
f
ec
ti
v
e
s
o
lu
tio
n
s
is
im
p
lem
en
tin
g
r
eten
tio
n
p
r
o
g
r
am
s
,
wh
ich
h
elp
m
ai
n
tain
co
m
p
eten
t ta
len
t,
in
cr
ea
s
e
p
r
o
d
u
ctiv
ity
,
an
d
r
e
d
u
ce
tu
r
n
o
v
er
-
r
elate
d
c
o
s
ts
[
4
]
.
T
r
ad
itio
n
al
p
r
ed
ictiv
e
m
o
d
els
u
s
e
b
in
ar
y
class
if
icatio
n
to
id
en
tify
em
p
lo
y
ee
s
lik
ely
t
o
leav
e
b
y
d
is
tin
g
u
is
h
in
g
b
etwe
en
th
o
s
e
wh
o
r
esig
n
a
n
d
th
o
s
e
wh
o
s
t
ay
,
en
ab
lin
g
o
r
g
an
izatio
n
s
to
p
r
io
r
itize
h
ig
h
-
r
is
k
em
p
lo
y
ee
s
f
o
r
r
ete
n
tio
n
[
5
]
.
I
n
th
ese
m
o
d
els
,
th
e
tar
g
et
v
ar
iab
le
is
b
in
ar
y
y
∈
{0
,
1
}.
Ho
wev
er
,
t
r
ad
itio
n
al
m
o
d
els
ca
n
n
o
t
ca
p
t
u
r
e
t
h
e
im
p
ac
t
o
f
r
eten
tio
n
in
ter
v
en
tio
n
s
,
wh
ile
u
p
lift
m
o
d
elin
g
esti
m
ates
th
e
in
cr
ea
s
e
d
p
r
o
b
a
b
ilit
y
o
f
em
p
lo
y
ee
s
s
tay
i
n
g
d
u
e
to
tr
ea
tm
en
t
[
6
]
.
I
n
th
e
co
n
tex
t
o
f
em
p
lo
y
ee
tu
r
n
o
v
er
,
u
p
lift
m
o
d
elin
g
c
o
m
p
ar
es
r
an
d
o
m
l
y
ass
ig
n
ed
tr
ea
tm
en
t
an
d
co
n
tr
o
l
g
r
o
u
p
s
to
ev
alu
ate
r
et
en
tio
n
ef
f
ec
tiv
en
ess
.
I
n
s
tead
o
f
ju
s
t
p
r
ed
ictin
g
wh
o
will
le
av
e,
th
is
a
p
p
r
o
ac
h
p
in
p
o
in
t
th
e
"p
e
r
s
u
ad
ab
le"
e
m
p
lo
y
ee
s
wh
o
ar
e
m
o
s
t
lik
ely
to
r
esp
o
n
d
p
o
s
itiv
ely
to
in
ter
v
en
tio
n
s
[
7
]
.
An
u
p
lift
m
o
d
el
is
a
p
r
ed
ictiv
e
m
o
d
el
d
esig
n
ed
to
esti
m
ate
th
e
ef
f
ec
t
o
f
tak
in
g
a
p
ar
ticu
lar
a
ctio
n
o
n
a
s
p
ec
if
ic
o
u
tco
m
e,
also
k
n
o
wn
as th
e
esti
m
atio
n
o
f
co
n
d
itio
n
al
a
v
er
ag
e
tr
ea
tm
en
t e
f
f
ec
ts
(
C
AT
E
)
[
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
I
n
teg
r
a
tin
g
F
-
filt
er a
n
d
K
-
M
ea
n
s
S
MOTE
to
en
h
a
n
ce
LGWUM
-
b
a
s
ed
… (
R
is
yd
a
Mif
ta
h
u
r
R
a
h
ma
h
)
1079
A
p
r
ev
io
u
s
s
tu
d
y
b
y
[
9
]
,
[
1
0
]
co
m
p
ar
ed
tr
a
d
itio
n
al
p
r
e
d
ictiv
e
an
d
u
p
lift
m
o
d
els
f
o
r
em
p
lo
y
ee
tu
r
n
o
v
e
r
u
s
in
g
lai’
s
g
e
n
er
aliz
ed
weig
h
ted
u
p
lift
m
eth
o
d
(
L
GW
UM
)
.
T
h
e
tr
ad
itio
n
al
m
o
d
el
u
s
es
a
b
in
ar
y
ch
u
r
n
tar
g
et,
wh
ile
th
e
u
p
lift
m
o
d
el
ap
p
lies
a
f
o
u
r
-
class
tr
ea
tm
en
t
-
co
n
tr
o
l ta
r
g
et,
en
ab
lin
g
b
etter
id
en
tific
atio
n
o
f
Per
s
u
ad
ab
le
em
p
lo
y
ee
s
an
d
m
o
r
e
ef
f
icien
t r
eten
tio
n
s
tr
ateg
ies.
A
m
ajo
r
ch
allen
g
e
i
n
u
p
lift
m
o
d
elin
g
is
h
ig
h
f
ea
tu
r
e
c
o
m
p
l
ex
ity
,
m
ak
in
g
f
ea
tu
r
e
s
elec
tio
n
ess
en
tial
to
r
ed
u
ce
o
v
er
f
itti
n
g
,
im
p
r
o
v
e
ef
f
icien
cy
,
a
n
d
en
h
a
n
ce
in
ter
p
r
etab
ilit
y
[
1
1
]
.
Ho
wev
e
r
,
t
r
ad
itio
n
al
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
ar
e
less
s
u
itab
le
f
o
r
u
p
lift
m
o
d
elin
g
b
ec
a
u
s
e
th
ey
f
o
cu
s
o
n
o
u
tco
m
e
p
r
ed
ictio
n
r
ath
er
th
a
n
tr
ea
tm
en
t
ef
f
ec
t
d
if
f
er
e
n
ce
s
b
etwe
en
tr
ea
ted
an
d
u
n
tr
ea
ted
g
r
o
u
p
s
[
1
2
]
.
Z
h
ao
et
a
l.
[
1
3
]
in
tr
o
d
u
ce
d
f
ea
t
u
r
e
s
elec
tio
n
m
eth
o
d
s
f
o
r
u
p
lift
m
o
d
elin
g
,
i
n
clu
d
in
g
F
-
f
ilter
,
L
R
-
f
ilter
,
an
d
em
b
ed
d
ed
ap
p
r
o
ac
h
es,
h
av
e
b
ee
n
in
tr
o
d
u
ce
d
to
s
elec
t th
e
m
o
s
t r
elev
an
t f
ea
tu
r
es,
im
p
r
o
v
i
n
g
ef
f
icien
cy
an
d
o
v
e
r
all
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
e
s
ec
o
n
d
c
h
allen
g
e
is
d
a
taset
im
b
alan
ce
,
wh
ic
h
h
in
d
er
s
lear
n
in
g
b
ec
a
u
s
e
m
o
s
t
s
u
p
er
v
is
ed
alg
o
r
ith
m
s
ass
u
m
e
b
alan
ce
d
class
es.
Alth
o
u
g
h
s
ev
e
r
al
s
o
lu
tio
n
s
ex
is
t,
s
y
n
t
h
etic
o
v
e
r
s
am
p
lin
g
is
o
f
ten
p
r
ef
er
r
e
d
as
it
ad
ju
s
ts
th
e
d
at
a
r
ath
er
th
a
n
th
e
class
if
ier
.
SMOT
E
[
1
4
]
,
is
a
co
m
m
o
n
o
v
er
s
am
p
lin
g
m
eth
o
d
th
at
g
en
e
r
ates
s
y
n
th
etic
m
in
o
r
ity
s
am
p
les
b
u
t
is
s
en
s
itiv
e
to
n
o
is
e
a
n
d
r
an
d
o
m
s
am
p
lin
g
[
1
5
]
.
T
o
o
v
e
r
co
m
e
th
ese
wea
k
n
ess
es,
[
1
6
]
in
t
r
o
d
u
ce
d
K
-
m
ea
n
s
SMOT
E
,
wh
ic
h
im
p
r
o
v
es
th
e
o
r
ig
in
al
SMO
T
E
th
r
o
u
g
h
a
th
r
ee
-
s
tag
e
p
r
o
ce
s
s
: c
lu
s
ter
in
g
,
f
ilter
in
g
,
an
d
o
v
e
r
s
am
p
lin
g
.
Nu
m
er
o
u
s
s
tu
d
ies
s
h
o
w
th
at
co
m
b
in
in
g
f
ea
tu
r
e
s
elec
tio
n
an
d
o
v
er
s
am
p
lin
g
ef
f
ec
tiv
ely
ad
d
r
ess
es
class
im
b
alan
ce
an
d
im
p
r
o
v
es
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
.
Hu
an
g
et
a
l.
[
1
7
]
d
em
o
n
s
tr
ated
th
at
th
e
in
teg
r
atio
n
o
f
in
f
o
r
m
atio
n
g
ain
-
b
ased
f
ea
tu
r
e
s
elec
tio
n
with
SMOT
E
o
u
tp
er
f
o
r
m
ed
th
e
in
d
iv
i
d
u
al
a
p
p
licatio
n
o
f
eith
er
tech
n
iq
u
e,
p
ar
ticu
lar
ly
w
h
en
f
ea
tu
r
e
s
elec
tio
n
p
r
ec
e
d
ed
o
v
er
s
am
p
lin
g
.
As
th
is
s
tu
d
y
u
s
es
u
p
lift
m
o
d
elin
g
,
f
ea
tu
r
e
s
elec
tio
n
tar
g
ets
h
eter
o
g
e
n
eo
u
s
tr
ea
tm
e
n
t
ef
f
ec
ts
.
T
h
er
ef
o
r
e,
th
e
F
-
Fil
ter
is
co
m
b
in
ed
with
K
-
Me
an
s
SMOT
E
to
ad
d
r
ess
im
b
alan
ce
ac
r
o
s
s
tr
ea
tm
en
t a
n
d
co
n
t
r
o
l
g
r
o
u
p
s
.
T
o
o
u
r
k
n
o
wled
g
e,
th
is
co
m
b
in
atio
n
h
as
n
o
t
b
e
en
ex
p
lo
r
ed
in
u
p
lift
-
b
ased
em
p
lo
y
ee
tu
r
n
o
v
e
r
p
r
ed
ictio
n
,
r
ep
r
esen
tin
g
a
n
o
v
el
co
n
tr
ib
u
tio
n
.
T
h
is
s
tu
d
y
m
ak
es th
r
ee
k
ey
co
n
tr
ib
u
tio
n
s
: (
1
)
a
p
p
ly
in
g
th
e
u
p
lift
-
s
p
ec
if
ic
F
-
Fil
ter
to
id
en
tify
f
ea
tu
r
es
d
r
iv
in
g
h
ete
r
o
g
e
n
eo
u
s
tr
ea
tm
e
n
t
ef
f
ec
ts
in
tu
r
n
o
v
e
r
;
(
2
)
in
te
g
r
atin
g
K
-
Me
an
s
SMOT
E
to
m
itig
ate
tr
ea
tm
en
t
-
co
n
tr
o
l
im
b
alan
ce
a
n
d
s
tab
ilize
u
p
lift;
an
d
(
3
)
ev
alu
atin
g
th
e
ap
p
r
o
ac
h
ac
r
o
s
s
m
u
ltip
le
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
r
o
b
u
s
tn
ess
.
C
o
ll
ec
tiv
ely
,
th
ese
co
n
tr
ib
u
tio
n
s
ad
v
an
ce
p
r
escr
ip
tiv
e
an
al
y
tics
f
o
r
em
p
lo
y
ee
r
eten
tio
n
,
im
p
r
o
v
i
n
g
u
p
lift
p
er
f
o
r
m
an
ce
an
d
e
n
ab
lin
g
ef
f
icie
n
t,
p
er
s
o
n
alize
d
in
ter
v
en
ti
o
n
a
llo
ca
tio
n
.
2.
T
H
E
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
er
e
ar
e
eig
h
t u
p
lift
m
o
d
elin
g
s
tr
ateg
ies
. I
n
th
is
s
tu
d
y
,
we
ad
o
p
t L
GW
UM
th
at
p
r
o
p
o
s
ed
b
y
[
1
8
]
, t
o
en
s
u
r
e
b
alan
ce
d
r
esp
o
n
s
e
m
ea
s
u
r
em
en
t,
u
p
lift
esti
m
atio
n
weig
h
ts
p
r
o
b
ab
ilit
ies
b
ased
o
n
t
r
ea
tm
en
t
an
d
c
o
n
tr
o
l
p
r
o
p
o
r
tio
n
s
.
He
r
e,
th
e
tr
ea
tm
en
t
is
d
ef
in
e
d
as
th
e
e
m
p
lo
y
ee
r
eten
tio
n
p
r
o
g
r
am
,
d
iv
id
in
g
s
u
b
jects
in
to
th
e
T
r
ea
tm
en
t
Gr
o
u
p
(
T
)
a
n
d
th
e
C
o
n
tr
o
l
Gr
o
u
p
(
C
)
.
E
m
p
lo
y
ee
r
esp
o
n
s
es
ar
e
cla
s
s
if
ied
ac
r
o
s
s
two
d
im
en
s
io
n
s
,
r
esp
o
n
s
e
o
u
tco
m
e
(
y
es/n
o
)
an
d
tr
ea
tm
e
n
t
ass
ig
n
m
en
t
(
y
es/n
o
)
,
r
esu
ltin
g
in
f
o
u
r
d
is
tin
ct
o
u
tco
m
e
g
r
o
u
p
s
v
is
u
alize
d
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Ma
p
p
in
g
e
m
p
lo
y
ee
r
esp
o
n
s
es a
n
d
r
eten
tio
n
p
r
o
g
r
a
m
ass
ig
n
m
en
t to
th
e
f
o
u
r
u
p
lif
t c
lass
es
E
m
p
lo
y
ee
Gr
o
u
p
C
lass
if
icatio
n
B
ased
o
n
T
r
ea
tm
e
n
t Res
p
o
n
s
e
[
9
]
:
a)
C
o
n
tr
o
l N
o
n
-
R
esp
o
n
d
er
s
(
C
N)
: E
m
p
lo
y
ee
s
wh
o
d
id
n
o
t r
ec
e
iv
e
tr
ea
tm
en
t a
n
d
lef
t th
e
co
m
p
an
y
.
b)
C
o
n
tr
o
l Res
p
o
n
d
er
s
(
C
R
)
: E
m
p
lo
y
ee
s
wh
o
s
tay
ed
with
o
u
t r
e
ce
iv
in
g
tr
e
atm
en
t.
c)
T
r
ea
ted
No
n
-
R
esp
o
n
d
er
s
(
T
N)
: E
m
p
lo
y
ee
s
wh
o
r
ec
eiv
ed
t
r
e
atm
en
t b
u
t sti
ll lef
t.
d)
T
r
ea
ted
R
esp
o
n
d
er
s
(
T
R
)
:
E
m
p
lo
y
ee
s
wh
o
s
tay
ed
af
ter
r
e
ce
iv
in
g
tr
ea
tm
en
t,
th
e
o
b
jectiv
e
is
to
d
etec
t
p
er
s
u
ad
ab
le
em
p
lo
y
ee
s
with
in
th
is
g
r
o
u
p
wh
o
tr
u
ly
b
e
n
ef
ite
d
f
r
o
m
tr
ea
t
m
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
1
0
7
8
-
1
0
8
6
1080
Af
ter
u
s
in
g
a
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
to
p
r
ed
ict
th
e
f
o
u
r
tar
g
et
class
es,
f
o
u
r
p
r
o
b
ab
ili
ty
v
alu
es
ar
e
p
r
o
d
u
ce
d
.
L
et
P
r
ep
r
esen
t
th
ese
p
r
o
b
ab
ilit
ies;
th
e
u
p
lift
s
co
r
e
is
th
e
n
co
m
p
u
ted
u
s
in
g
th
e
L
GW
UM
m
eth
o
d
as f
o
llo
ws
[
9
]
(
1
)
:
Up
lift
S
co
r
e
=
(
|
)
(
)
)
+
(
|
)
(
)
)
-
(
|
)
(
)
)
-
(
|
)
(
)
(
1
)
T
h
e
u
p
lift
s
co
r
e
is
h
ig
h
wh
e
n
P(CN
∣
x
)
an
d
P(T
R
∣
x
)
ar
e
h
ig
h
,
an
d
P(CR
∣
x
)
an
d
P(T
N
∣
x
)
ar
e
lo
w,
in
d
icatin
g
th
at
t
h
e
em
p
l
o
y
ee
lik
ely
b
elo
n
g
s
to
t
h
e
Per
s
u
ad
ab
les
g
r
o
u
p
a
n
d
s
h
o
u
ld
b
e
p
r
io
r
it
ized
f
o
r
th
e
r
eten
tio
n
p
r
o
g
r
a
m
.
E
m
p
lo
y
ee
s
ar
e
ca
teg
o
r
ize
d
in
to
f
o
u
r
u
p
lif
t c
lass
es
:
a)
Per
s
u
ad
ab
les:
Stay
if
tr
ea
ted
; le
av
e
if
n
o
t
(
p
r
im
a
r
y
tar
g
et
)
.
b)
Su
r
e
T
h
i
n
g
s
: Stay
r
eg
ar
d
less
o
f
tr
ea
tm
en
t.
c)
L
o
s
t Cau
s
es: L
ea
v
e
r
eg
ar
d
less
o
f
tr
ea
tm
en
t.
d)
Do
-
No
t
-
Dis
tu
r
b
: D
r
iv
en
awa
y
(
leav
e)
if
tr
ea
te
d
.
2
.
1
.
F
-
F
ilte
r:
m
et
ho
d f
o
r
s
elec
t
ing
f
ea
t
ures in upli
f
t
m
o
delin
g
F
e
a
t
u
r
e
s
e
l
e
ct
i
o
n
p
l
a
y
s
a
m
o
r
e
c
r
i
t
i
ca
l
r
o
l
e
i
n
u
p
li
f
t
m
o
d
e
l
in
g
t
h
a
n
i
n
s
t
a
n
d
a
r
d
p
r
e
d
i
c
ti
v
e
m
o
d
e
l
i
n
g
[
1
2
]
.
Z
h
a
o
e
t
a
l
.
[
1
3
]
i
n
t
r
o
d
u
c
ed
t
h
e
F
-
F
il
t
e
r
a
n
d
L
R
-
F
il
t
e
r
,
wh
i
c
h
e
v
a
l
u
a
te
f
e
at
u
r
e
r
e
l
e
v
a
n
ce
u
s
i
n
g
p
-
v
a
l
u
es
a
n
d
s
t
a
ti
s
t
i
c
al
s
i
g
n
i
f
i
c
a
n
c
e
.
I
n
t
h
e
F
-
F
i
l
te
r
,
f
e
a
t
u
r
e
i
m
p
o
r
t
a
n
c
e
i
s
q
u
a
n
t
i
f
i
e
d
u
s
i
n
g
t
h
e
F
-
s
t
a
ti
s
t
i
c
as
d
e
f
i
n
e
d
i
n
(
2
)
.
F =
(
−
′
)
/
′
/
(
−
−
2
)
(
2
)
I
n
th
is
f
o
r
m
u
latio
n
,
N
is
th
e
to
tal
n
u
m
b
er
o
f
o
b
s
er
v
atio
n
s
,
R
S
S
′
is
th
e
R
esid
u
al
Su
m
o
f
Sq
u
ar
es o
f
th
e
m
o
d
el
with
esti
m
ated
co
e
f
f
i
cien
ts
,
an
d
RS
S
r
e
p
r
esen
ts
th
e
b
aselin
e
m
o
d
el.
I
is
th
e
tr
ea
tm
en
t
in
d
icato
r
(
1
=
tr
ea
tm
e
n
t,
0
=
co
n
tr
o
l)
,
R
is
th
e
p
o
l
y
n
o
m
ial
d
eg
r
ee
,
d
en
o
tes
th
e
f
ea
tu
r
e
r
aised
to
t
h
e
r
-
th
p
o
wer
,
an
d
ε
is
th
e
er
r
o
r
ter
m
.
T
h
e
p
a
r
am
eter
s
α
,
β
,
an
d
θ
ar
e
m
o
d
e
l
co
ef
f
icien
ts
,
with
θ
in
d
icatin
g
th
e
m
ag
n
itu
d
e
o
f
th
e
h
eter
o
g
e
n
eo
u
s
tr
ea
tm
e
n
t e
f
f
ec
t o
f
f
ea
tu
r
e
.
2
.
2
.
K
-
M
ea
ns
SM
O
T
E
K
-
M
ea
n
s
SMOT
E
,
in
tr
o
d
u
ce
d
b
y
[
1
6
]
,
in
v
o
lv
es
clu
s
ter
in
g
th
e
d
ata
u
s
in
g
K
-
m
ea
n
s
,
s
elec
tin
g
m
in
o
r
ity
-
d
en
s
e
clu
s
ter
s
f
o
r
o
v
er
s
am
p
lin
g
,
an
d
ap
p
ly
i
n
g
SM
OT
E
with
in
th
o
s
e
clu
s
ter
s
.
Sy
n
th
etic
s
am
p
les
ar
e
g
en
er
ated
p
r
o
p
o
r
tio
n
ally
,
p
r
io
r
itizin
g
clu
s
ter
s
with
s
p
ar
s
er
m
in
o
r
ity
d
is
tr
ib
u
tio
n
s
to
im
p
r
o
v
e
class
b
alan
ce
.
K
-
M
ea
n
s
is
an
iter
ativ
e
clu
s
te
r
in
g
al
g
o
r
ith
m
th
at
ass
ig
n
s
p
o
in
ts
to
th
e
n
ea
r
est
ce
n
tr
o
id
an
d
u
p
d
ates
ce
n
tr
o
id
s
as
th
e
m
ea
n
o
f
ass
ig
n
ed
p
o
in
ts
u
n
til
co
n
v
e
r
g
en
ce
to
a
lo
ca
l
o
p
tim
u
m
[
1
9
]
.
Hair
an
i
et
a
l.
[
2
0
]
ha
ve
sh
o
wn
th
at
K
-
M
ea
n
s
SMOT
E
en
h
an
ce
s
class
if
icatio
n
p
er
f
o
r
m
an
ce
an
d
m
in
o
r
ity
s
am
p
le
q
u
ality
wh
ile
p
r
eser
v
in
g
d
ata
s
tr
u
ctu
r
e,
alb
eit
with
in
cr
ea
s
ed
co
m
p
u
tatio
n
al
co
s
t.
T
h
is
s
tu
d
y
co
m
b
in
es
F
-
Fil
ter
f
ea
tu
r
e
s
elec
tio
n
an
d
K
-
Me
an
s
SMOT
E
to
im
p
r
o
v
e
u
p
lift
m
o
d
elin
g
.
A
f
ter
s
p
litt
in
g
th
e
d
ata,
F
-
Fi
lter
s
elec
ts
f
ea
tu
r
es
r
elate
d
t
o
tr
ea
tm
en
t
-
ef
f
ec
t
h
eter
o
g
en
e
ity
,
an
d
K
-
Me
an
s
SMOT
E
is
ap
p
lied
s
ep
ar
ately
to
tr
ea
tm
en
t
a
n
d
co
n
tr
o
l
g
r
o
u
p
s
to
h
a
n
d
le
im
b
alan
c
e.
T
h
e
b
alan
ce
d
d
ata
ar
e
th
en
u
s
ed
to
t
r
ain
m
o
d
els,
an
d
L
GW
UM
co
m
p
u
tes u
p
lift
s
co
r
es to
esti
m
ate
in
d
iv
id
u
al
tr
ea
t
m
en
t e
f
f
ec
ts
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
tu
d
y
i
n
v
o
lv
es
d
at
a
cl
ea
n
i
n
g
,
f
e
at
u
r
e
e
n
g
i
n
ee
r
i
n
g
t
o
c
r
e
a
te
a
d
d
iti
o
n
a
l
f
ea
t
u
r
es
b
as
ed
o
n
t
r
e
at
m
e
n
t
an
d
c
o
n
tr
o
l
g
r
o
u
p
s
,
as
we
ll
as
u
p
li
f
t
r
esp
o
n
s
e
f
ea
tu
r
es
.
T
h
e
r
e
s
ea
r
c
h
m
e
th
o
d
o
l
o
g
y
is
ill
u
s
t
r
a
t
ed
i
n
F
ig
u
r
e
2
.
Fig
u
r
e
2
.
Flo
wch
ar
t a
n
d
p
r
o
p
o
s
ed
m
eth
o
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
I
n
teg
r
a
tin
g
F
-
filt
er a
n
d
K
-
M
ea
n
s
S
MOTE
to
en
h
a
n
ce
LGWUM
-
b
a
s
ed
… (
R
is
yd
a
Mif
ta
h
u
r
R
a
h
ma
h
)
1081
3
.
1
.
Da
t
a
a
cquis
it
io
n
Data
s
et
1
is
a
s
y
n
t
h
etic
h
u
m
a
n
r
eso
u
r
ce
s
d
ataset
with
1
0
f
e
atu
r
es
an
d
a
b
in
ar
y
tar
g
et
v
a
r
iab
le
(
Left
)
,
co
n
s
is
tin
g
o
f
3
,
5
7
1
em
p
lo
y
ee
s
wh
o
lef
t
an
d
1
1
,
4
2
8
wh
o
d
i
d
n
o
t,
f
o
r
a
to
tal
o
f
1
4
,
9
9
9
r
ec
o
r
d
s
.
In
[
2
1
]
,
Data
s
et
2
is
a
s
y
n
th
etic
d
ataset
f
r
o
m
I
B
M
W
at
s
o
n
An
aly
tics
with
3
5
f
ea
tu
r
es
an
d
a
b
in
a
r
y
tar
g
et
(
A
ttr
itio
n
)
,
co
n
tain
in
g
2
3
7
e
m
p
lo
y
ee
s
wh
o
lef
t
an
d
1
,
2
3
3
wh
o
d
id
n
o
t,
to
talin
g
1
,
4
7
0
r
ec
o
r
d
s
[
2
2
]
,
an
d
Data
s
et
3
is
a
r
ea
l
-
wo
r
ld
d
ataset
with
1
6
f
ea
tu
r
es
an
d
a
b
in
ar
y
tar
g
e
t
(
E
ve
n
t
)
,
c
o
m
p
r
is
in
g
4
2
6
e
m
p
lo
y
ee
s
wh
o
lef
t
an
d
3
8
9
wh
o
d
id
n
o
t,
r
esu
ltin
g
in
8
1
5
r
ec
o
r
d
s
[
2
3
]
.
3
.
2
.
Da
t
a
prepro
ce
s
s
ing
Data
p
r
ep
r
o
ce
s
s
in
g
was
p
e
r
f
o
r
m
ed
b
y
h
a
n
d
lin
g
m
is
s
in
g
v
alu
es
an
d
tr
an
s
f
o
r
m
in
g
ca
teg
o
r
ical
d
ata
in
to
n
u
m
er
ical
f
o
r
m
u
s
in
g
lab
el
en
co
d
in
g
a
n
d
o
n
e
-
h
o
t
en
c
o
d
in
g
,
i
n
cr
ea
s
in
g
th
e
n
u
m
b
er
o
f
f
ea
tu
r
es f
r
o
m
1
0
to
1
9
in
Data
s
et
1
,
3
1
to
5
0
in
Da
taset 2
,
an
d
1
6
to
5
2
in
Data
s
e
t 3
.
3
.
3
.
Uplift
m
o
del da
t
a
prepa
ra
t
io
n
Sin
ce
th
e
d
atasets
lack
an
in
h
er
en
t
tr
ea
tm
en
t
-
co
n
tr
o
l
f
ea
t
u
r
e,
th
e
t
r
ea
tm
en
t
v
ar
iab
le
was
d
eter
m
in
ed
b
ased
o
n
th
r
ee
cr
iter
ia
f
r
o
m
[
9
]
:
ac
tio
n
a
b
ilit
y
,
co
r
r
elatio
n
with
th
e
tar
g
et,
an
d
co
n
tr
o
l
g
r
o
u
p
a
v
ailab
ilit
y
.
B
ased
o
n
th
ese
cr
iter
ia,
th
e
s
elec
ted
tr
ea
tm
en
t
v
ar
ia
b
le
s
f
o
r
ea
c
h
d
ataset
ar
e
“P
r
o
m
o
t
io
n
”
f
o
r
Data
s
et
1
(
co
m
p
r
is
in
g
3
1
9
tr
ea
tm
en
t
an
d
1
4
,
6
8
0
co
n
tr
o
l
s
am
p
les;
tr
ea
tm
en
t
co
r
r
elatio
n
:
0
.
0
6
2
)
,
“Ov
er
tim
e”
f
o
r
Data
s
et
2
(
1
,
0
5
4
tr
ea
tm
en
t
an
d
4
1
6
co
n
tr
o
l
s
am
p
les;
tr
ea
tm
en
t
co
r
r
elatio
n
:
0
.
2
4
6
)
,
an
d
“C
o
ac
h
in
g
”
f
o
r
Data
s
et
3
(
6
8
3
tr
ea
tm
en
t
a
n
d
1
3
2
co
n
tr
o
l
s
am
p
les;
tr
ea
tm
en
t
co
r
r
elatio
n
:
0
.
0
6
4
)
.
Prio
r
s
tu
d
ies
s
h
o
w
t
h
ese
in
ter
v
en
tio
n
s
ca
n
r
ed
u
ce
tu
r
n
o
v
e
r
an
d
im
p
r
o
v
e
em
p
l
o
y
ee
r
eten
tio
n
.
Pro
m
o
tio
n
in
cr
ea
s
es
m
o
r
ale
an
d
p
er
f
o
r
m
an
ce
,
r
ed
u
cin
g
tu
r
n
o
v
e
r
[
2
4
]
,
m
an
ag
in
g
o
v
e
r
tim
e
lo
wer
s
jo
b
s
tr
ess
an
d
t
u
r
n
o
v
er
in
ten
tio
n
[
2
5
]
,
a
n
d
co
ac
h
in
g
im
p
r
o
v
es
en
g
ag
em
e
n
t
an
d
co
m
m
itm
en
t
,
s
tr
en
g
th
en
in
g
r
eten
tio
n
[
2
6
]
.
T
h
e
m
o
d
el
u
s
es
f
o
u
r
en
co
d
ed
tar
g
et
class
es:
C
N,
C
R
,
T
N,
an
d
T
,
wh
ic
h
ar
e
s
u
b
s
eq
u
e
n
tly
en
co
d
ed
as
0
,
1
,
2
,
a
n
d
3
,
r
esp
ec
ti
v
ely
.
Af
ter
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
th
e
d
ataset
was s
p
lit in
to
tr
ain
in
g
an
d
test
in
g
s
e
ts
u
s
in
g
a
7
0
:3
0
r
atio
.
3
.
4
.
F
e
a
t
ure
s
elec
t
io
n
Featu
r
e
s
elec
tio
n
was
co
n
d
u
c
ted
b
y
r
an
k
in
g
f
ea
tu
r
es
u
s
in
g
F
-
s
co
r
es
an
d
p
-
v
al
u
es,
th
en
iter
ativ
ely
r
em
o
v
in
g
th
e
lo
west
-
r
an
k
e
d
f
ea
tu
r
es
in
s
tep
s
o
f
f
iv
e.
E
a
ch
s
u
b
s
et
was
ev
alu
ated
o
n
th
e
test
d
ata
u
s
in
g
XGBo
o
s
t
to
ass
es
s
ch
an
g
es
in
u
p
lift
p
er
f
o
r
m
an
ce
v
ia
t
h
e
Qin
i
co
ef
f
icien
t.
Featu
r
e
im
p
o
r
tan
ce
was
also
r
ep
o
r
ted
at
ea
ch
s
tag
e
to
i
d
en
ti
f
y
in
f
lu
e
n
tial p
r
ed
icto
r
s
.
R
esu
lts
ar
e
s
u
m
m
ar
ized
in
T
a
b
le
1
.
T
ab
le
1
.
E
x
p
er
im
en
t t
o
d
eter
m
in
e
th
e
o
p
tim
al
n
u
m
b
e
r
o
f
f
ea
t
u
r
es in
d
ataset
1
D
a
t
a
s
e
t
M
e
t
r
i
c
s
A
l
l
F
e
a
t
u
r
e
s
45
40
35
30
25
20
15
10
5
D
a
t
a
s
e
t
1
(
1
6
)
Q
i
n
i
C
o
e
f
f
i
c
i
e
nt
0
,
1
8
5
5
-
-
-
-
-
-
0
,
1
8
4
9
0
,
1
2
6
0
0
,
1
0
4
0
FI
-
S
c
o
r
e
0
,
0
3
2
0
5
0
-
-
-
-
-
-
0
,
0
7
7
7
6
5
0
,
7
2
3
4
1
8
3
,
3
3
1
3
0
4
D
a
t
a
s
e
t
2
(
4
7
)
Q
i
n
i
C
o
e
f
f
i
c
i
e
nt
0
,
0
4
7
9
0
,
0
4
8
5
0
,
0
2
6
7
0
.
0
4
2
3
0
,
0
6
3
4
0
,
0
2
9
5
0
,
0
1
6
5
0
,
0
1
2
8
0
,
0
1
1
4
-
0
,
0
0
8
4
FI
-
S
c
o
r
e
0
,
0
0
0
0
8
3
0
,
0
0
0
2
57
0
,
0
7
8
2
5
6
0
,
2
3
7
6
7
8
0
,
8
1
2
1
6
7
1
,
1
3
0
7
3
9
1
,
4
4
2
8
1
7
2
,
6
4
7
0
8
5
4
,
0
1
7
0
8
6
8
,
6
2
7
0
3
8
D
a
t
a
s
e
t
3
(
4
9
)
Q
i
n
i
C
o
e
f
f
i
c
i
e
nt
0
,
0
6
0
8
0
,
0
6
0
5
0
,
0
6
8
3
0
,
0
7
5
4
0
,
0
3
3
3
0
,
0
5
6
7
0
,
0
5
2
3
0
,
0
7
2
3
0
,
0
4
2
9
0
,
0
3
0
2
FI
-
S
c
o
r
e
0
,
0
0
4
3
0
6
0
,
0
1
5
4
55
0
,
0
7
1
2
9
8
0
,
1
3
1
2
2
9
0
,
3
1
3
0
5
7
0
,
4
3
0
2
1
2
0
,
8
9
2
1
5
3
1
,
3
4
1
7
0
2
1
,
8
7
3
3
9
0
2
,
6
4
7
0
9
1
I
n
Data
s
et
1
,
f
ea
tu
r
e
s
elec
tio
n
was
lim
ited
to
1
5
f
ea
tu
r
es
d
u
e
t
o
its
s
m
aller
s
ize
(
1
6
f
ea
tu
r
es),
co
m
p
ar
ed
with
Data
s
et
2
(
4
7
f
ea
tu
r
es)
an
d
Data
s
et
3
(
4
9
f
e
atu
r
es).
Gr
ad
u
al
f
ea
tu
r
e
r
e
d
u
c
tio
n
in
s
tep
s
o
f
f
iv
e
s
h
o
ws
th
at
r
em
o
v
i
n
g
less
in
f
o
r
m
ativ
e
f
ea
tu
r
es
im
p
r
o
v
es
u
p
l
if
t
p
er
f
o
r
m
an
ce
u
p
to
an
o
p
ti
m
al
p
o
in
t.
B
ey
o
n
d
th
is
th
r
esh
o
ld
,
elim
in
atin
g
in
f
lu
en
tial
p
r
e
d
icto
r
s
r
e
d
u
ce
s
th
e
Qin
i
co
e
f
f
icien
t,
as
r
ef
lecte
d
b
y
r
is
in
g
f
ea
tu
r
e
im
p
o
r
tan
ce
v
alu
es.
Ov
er
all,
r
esu
lts
in
d
icate
th
at
ea
ch
d
ata
s
et
h
as
an
o
p
tim
al
f
ea
tu
r
e
s
u
b
s
et
wh
er
e
u
p
lift
p
er
f
o
r
m
an
ce
is
m
ax
im
ized
u
s
in
g
th
e
m
i
n
im
al
s
et
o
f
im
p
ac
tf
u
l f
ea
tu
r
es.
3
.
5
.
H
a
nd
lin
g
im
ba
la
nced
da
t
a
B
ef
o
r
e
m
o
d
elin
g
,
th
e
class
im
b
alan
ce
was
ad
d
r
ess
ed
u
s
in
g
th
e
K
-
Me
an
s
SMOT
E
m
eth
o
d
[
1
4
]
.
T
o
ad
d
r
ess
class
im
b
alan
ce
,
K
-
Me
an
s
SMOT
E
wa
s
ap
p
lied
ac
r
o
s
s
th
r
ee
d
atasets
wi
th
v
ar
y
in
g
s
tr
ateg
ies
b
ased
o
n
t
h
e
s
ev
er
ity
o
f
th
e
i
m
b
alan
ce
.
I
n
Data
s
et
1
,
wh
ich
s
u
f
f
er
e
d
f
r
o
m
s
ev
er
e
im
b
alan
ce
,
m
in
o
r
ity
class
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
1
0
7
8
-
1
0
8
6
1082
wer
e
o
v
er
s
am
p
led
c
o
n
s
er
v
ati
v
ely
b
u
t
r
em
ai
n
ed
s
tr
atif
ied
(
in
cr
ea
s
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g
C
lass
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o
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d
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o
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0
)
ag
ain
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t
a
m
ajo
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ity
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lass
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o
f
7
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7
9
7
.
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o
n
v
er
s
ely
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f
o
r
Data
s
ets
2
an
d
3
wh
ic
h
ex
h
i
b
ited
m
o
d
er
ate
im
b
alan
ce
,
all
m
in
o
r
ity
class
es
wer
e
o
v
er
s
am
p
led
to
s
u
cc
ess
f
u
lly
m
atch
th
eir
r
esp
ec
tiv
e
m
ajo
r
ity
class
b
aselin
es,
b
r
in
g
i
n
g
Data
s
et
2
class
es
to
an
ev
en
d
is
tr
ib
u
tio
n
o
f
ap
p
r
o
x
im
ately
6
5
9
in
s
tan
ce
s
ea
ch
,
an
d
Data
s
et
3
class
es to
r
o
u
g
h
ly
2
5
7
in
s
tan
ce
s
ea
ch
.
3
.
6
.
E
v
a
lua
t
i
o
n
T
r
ad
itio
n
al
m
etr
ics
lik
e
ac
cu
r
ac
y
ev
alu
ate
p
r
ed
ictio
n
co
r
r
e
ctn
ess
at
th
e
in
d
iv
id
u
al
lev
el,
wh
ich
is
u
n
s
u
itab
le
f
o
r
u
p
lift
m
o
d
elin
g
b
ec
au
s
e
in
d
iv
id
u
al
u
p
lift
ca
n
n
o
t
b
e
d
ir
ec
tly
o
b
s
er
v
ed
,
o
n
e
p
er
s
o
n
ca
n
n
o
t
b
e
b
o
th
in
th
e
tr
ea
tm
e
n
t
an
d
co
n
tr
o
l
g
r
o
u
p
s
.
T
h
er
ef
o
r
e,
u
p
lift
m
u
s
t
b
e
m
ea
s
u
r
ed
at
th
e
s
eg
m
en
t
lev
el,
an
d
th
e
Qin
i
co
ef
f
icien
t,
in
tr
o
d
u
ce
d
in
[
2
7
]
an
d
ad
ap
te
d
f
r
o
m
t
h
e
Gin
i
co
ef
f
icien
t,
is
u
s
ed
to
ev
alu
ate
u
p
lift
p
er
f
o
r
m
an
ce
.
T
h
r
o
u
g
h
e
q
u
atio
n
,
u
p
lift
ca
n
b
e
n
o
r
m
alize
d
in
(
3
)
.
(
)
=
(
−
)
(
3
)
Her
e,
r
e
p
r
esen
ts
th
e
p
r
o
p
o
r
tio
n
o
f
tar
g
ets
in
th
e
tr
ea
tm
en
t
g
r
o
u
p
,
an
d
N
d
e
n
o
tes
th
e
to
tal
p
o
p
u
latio
n
.
A
Seab
o
r
n
lin
e
p
l
o
t
is
u
s
ed
to
v
is
u
alize
th
e
Qin
i
cu
r
v
e
f
o
r
th
e
u
p
lift
m
o
d
el,
with
a
r
an
d
o
m
m
o
d
el
cu
r
v
e
ad
d
ed
f
o
r
c
o
m
p
ar
is
o
n
[
9]
(
4
)
.
=
∑
−
−
1
=
0
(
4
)
T
o
ev
alu
ate
w
h
eth
er
a
m
o
d
el
s
u
cc
ess
f
u
lly
tar
g
ets
th
e
r
ig
h
t
em
p
lo
y
ee
s
,
t
h
e
m
o
d
el’
s
Qin
i
cu
r
v
e
is
co
m
p
ar
ed
with
th
at
o
f
a
r
a
n
d
o
m
m
o
d
el
.
T
h
er
ef
o
r
e,
a
m
o
d
el
is
co
n
s
id
er
ed
ef
f
ec
tiv
e
if
it
ac
h
iev
es
a
Qin
i
co
ef
f
icien
t g
r
ea
te
r
th
an
0
.
0
5
[
9
]
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
Q
ini
curv
e
a
nd
qin
i c
o
ef
f
icient
T
h
e
s
tu
d
y
u
tili
ze
s
f
iv
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
(
L
o
g
is
tic
R
eg
r
ess
io
n
,
XGBo
o
s
t,
R
an
d
o
m
Fo
r
est,
SVM,
an
d
B
alan
ce
d
R
an
d
o
m
Fo
r
est)
to
g
en
e
r
ate
class
p
r
o
b
ab
ilit
ies,
wh
ich
ar
e
th
en
co
n
v
er
ted
in
to
r
an
k
e
d
u
p
lift
s
co
r
es.
Ultim
ately
,
m
o
d
el
p
er
f
o
r
m
an
ce
is
ev
alu
ated
a
g
ain
s
t
a
r
an
d
o
m
b
en
c
h
m
ar
k
u
s
in
g
th
e
Qin
i
cu
r
v
e
an
d
Qin
i c
o
ef
f
icien
t,
s
er
v
in
g
as th
e
s
tan
d
ar
d
ev
alu
atio
n
m
etr
ics f
o
r
u
p
lift
m
o
d
elin
g
[
2
8
]
.
Fig
u
r
e
3
d
is
p
lay
s
th
e
Qin
i c
u
r
v
e
an
d
Qin
i c
o
e
f
f
icien
t f
o
r
Data
s
et
1
.
W
it
hout
F
e
a
tu
r
e
S
e
lec
ti
on
(
16
f
e
a
tur
e
s
)
L
R
:
0.
0558
XG
B
:
0.
1855
R
F
:
0.
0519
S
VM
:
0.
1118
B
R
F
:
0.
0726
F
e
a
tur
e
S
e
lec
ti
o
n
(
15
f
e
a
tu
r
e
s
)
L
R
:
0,
0497
XG
B
:
0.
1686
R
F
:
0.
0544
S
VM
:
0.
1028
B
R
F
:
0.
1076
F
e
a
tur
e
S
e
lec
ti
o
n
(
15
f
e
a
tu
r
e
s
)
+
KM
e
a
ns
S
M
OT
E
L
R
:
0,
0845
XG
B
:
0,
1490
R
F
:
0,
0732
S
VM
:
0,
0479
B
R
F
:
0,
1023
Fig
u
r
e
3
.
Qin
i
c
u
r
v
e
a
n
d
q
i
n
i
co
ef
f
icien
t f
o
r
d
ataset
1
Fo
r
Data
s
et
1
,
XGBo
o
s
t
co
n
s
is
ten
tly
ac
h
iev
es
th
e
h
ig
h
est
Qin
i
co
ef
f
icien
t,
d
em
o
n
s
tr
atin
g
s
tr
o
n
g
ca
p
ab
ilit
y
in
ca
p
tu
r
in
g
h
eter
o
g
en
e
o
u
s
tr
ea
tm
en
t
ef
f
ec
t
s
.
Featu
r
e
s
elec
tio
n
alo
n
e
s
lig
h
tly
d
eg
r
ad
es
p
er
f
o
r
m
an
ce
,
wh
ile
its
co
m
b
in
ati
o
n
with
K
-
Me
an
s
SMO
T
E
im
p
r
o
v
es
u
p
lift
r
esu
lts
f
o
r
s
ev
er
al
m
o
d
els,
u
n
d
er
s
co
r
i
n
g
th
e
im
p
o
r
tan
ce
o
f
b
alan
ce
d
tr
ea
tm
en
t
r
ep
r
e
s
en
tatio
n
.
Fig
u
r
e
4
s
h
o
ws
t
h
e
Qin
i
cu
r
v
e
a
n
d
co
ef
f
icien
t f
o
r
Data
s
et
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
I
n
teg
r
a
tin
g
F
-
filt
er a
n
d
K
-
M
ea
n
s
S
MOTE
to
en
h
a
n
ce
LGWUM
-
b
a
s
ed
… (
R
is
yd
a
Mif
ta
h
u
r
R
a
h
ma
h
)
1083
W
it
hout
F
e
a
tu
r
e
S
e
lec
ti
on
L
R
:
0,
0394
XG
B
:
0,
0479
R
F
:
0,
0494
S
VM
:
0,
0459
B
R
F
:
0,
0144
F
e
a
tur
e
S
e
lec
ti
o
n
(
30
f
e
a
tu
r
e
s
)
L
R
:
0,
0430
XG
B
:
0,
0634
R
F
:
0,
0457
S
VM
:
0,
0578
B
R
F
:
0,
0304
F
e
a
tur
e
S
e
lec
ti
o
n
(
30
f
e
a
tu
r
e
s
)
+
KM
e
a
ns
S
M
OT
E
L
R
:
-
0,
0112
XG
B
:
0,
0755
R
F
:
0,
0487
S
VM
:
0,
0474
B
R
F
:0,
0455
Fig
u
r
e
4
.
Qin
i
c
u
r
v
e
a
n
d
q
i
n
i
co
ef
f
icien
t f
o
r
d
ataset
2
Fo
r
Data
s
et
2
,
u
p
lift
p
e
r
f
o
r
m
an
ce
im
p
r
o
v
es
with
f
ea
tu
r
e
s
elec
tio
n
,
in
d
icatin
g
th
at
r
em
o
v
in
g
ir
r
elev
an
t
f
ea
tu
r
es
en
h
an
ce
s
t
r
ea
tm
en
t
-
ef
f
ec
t
lear
n
in
g
.
C
o
m
b
in
in
g
f
ea
tu
r
e
s
elec
tio
n
wit
h
K
-
Me
an
s
SMOT
E
p
r
o
d
u
ce
s
th
e
h
ig
h
est
Qin
i
s
co
r
es,
with
XGBo
o
s
t
ag
ain
p
e
r
f
o
r
m
i
n
g
b
est,
d
e
m
o
n
s
tr
atin
g
r
o
b
u
s
tn
ess
to
b
o
th
d
im
en
s
io
n
ality
r
ed
u
ctio
n
an
d
class
im
b
alan
ce
.
Fig
u
r
e
5
p
r
esen
ts
th
e
Qin
i
cu
r
v
e
an
d
Qin
i
co
ef
f
icien
t
f
o
r
Data
s
et
3
.
W
it
hout
F
e
a
tu
r
e
S
e
lec
ti
on
L
R
:
0.
0015
XG
B
:
0.
0832
R
F
:
0.
0736
S
VM
:
0.
0685
B
R
F
:
0.
0755
F
e
a
tur
e
S
e
lec
ti
o
n
(
35
f
e
a
tu
r
e
s
)
L
R
:
0,
0353
XG
B
:
0.
0649
R
F
:
0.
0865
S
VM
:
0.
0598
B
R
F
:
0.
0726
F
e
a
tur
e
S
e
lec
ti
o
n
(
35
f
e
a
tu
r
e
s
)
+
KM
e
a
ns
S
M
OT
E
L
R
:
0,
0870
XG
B
:
0,
0577
R
F
:
0,
0726
S
VM
:
0,
0689
B
R
F
:
0,
0569
Fig
u
r
e
5
.
Qin
i
c
u
r
v
e
a
n
d
q
i
n
i
co
ef
f
icien
t f
o
r
d
ataset
3
Fo
r
Data
s
et
3
,
f
ea
tu
r
e
s
elec
tio
n
im
p
r
o
v
es
u
p
lift
p
e
r
f
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1
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