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379
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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A
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ti
f
I
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tell
,
Vo
l.
9
,
No
.
3
,
Sep
te
m
b
er
20
20
:
3
7
9
–
386
380
I
n
t
h
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Ma
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[8
-
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h
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.
I
t
is
i
i
m
p
o
r
tan
t
to
r
ea
li
s
e
th
at
v
al
u
er
s
f
a
ce
v
ar
io
u
s
c
h
alle
n
g
e
s
b
ec
au
s
e
o
f
th
eir
h
ea
v
y
d
ep
en
d
en
c
y
y
o
n
m
ar
k
et
d
ata.
L
ac
k
o
f
d
ata
m
ea
n
s
lac
k
o
f
s
u
p
p
o
r
t f
o
r
th
e
v
al
u
ab
le
co
n
tr
ib
u
tio
n
s
o
f
g
r
ee
n
a
ttrib
u
tes,
w
h
ich
is
s
u
p
p
o
s
ed
to
b
e
th
e
f
ac
to
r
i
n
f
lu
e
n
ci
n
g
th
e
GB
p
r
ice.
I
n
d
ee
d
,
th
e
r
ea
l
estate
m
ar
k
et
i
s
e
x
p
o
s
ed
to
m
an
y
p
r
ice
f
l
u
ct
u
atio
n
s
d
u
e
to
e
x
is
t
in
g
co
r
r
elatio
n
s
w
it
h
m
an
y
v
ar
iab
les
a
n
d
s
o
m
e
o
f
w
h
ic
h
ar
e
b
ey
o
n
d
o
u
r
co
n
tr
o
l o
r
p
er
h
ap
s
u
n
k
n
o
w
n
[
1
4
]
.
I
n
li
g
h
t
o
f
t
h
i
s
s
i
tu
atio
n
,
Ma
c
h
in
e
lear
n
i
n
g
(
ML
)
m
o
d
el
h
as
e
m
er
g
ed
a
s
a
v
er
y
p
r
o
m
i
s
i
n
g
ap
p
r
o
ac
h
in
r
eso
l
v
in
g
t
h
e
i
s
s
u
e
an
d
it
is
p
r
o
v
e
n
to
b
e
e
f
f
ec
t
iv
e
in
d
if
f
er
e
n
t
k
i
n
d
s
o
f
p
r
ed
ictio
n
an
d
class
if
icatio
n
p
r
o
b
lem
[
1
5
-
17]
.
ML
m
o
d
el
h
as
d
if
f
er
en
t
k
i
n
d
s
o
f
al
g
o
r
ith
m
s
a
n
d
tech
n
iq
u
e
s
to
b
e
s
elec
ted
f
o
r
d
ev
elo
p
in
g
a
g
o
o
d
p
r
ed
icto
r
m
o
d
e
l.
T
h
ese
ar
e
b
en
ef
icia
l
to
r
eso
l
v
e
d
ata
s
et
p
r
o
b
le
m
s
s
u
c
h
a
s
i
m
b
ala
n
ce
an
d
i
n
s
u
f
f
icien
t
d
ata
lik
e
t
h
e
li
m
itat
io
n
o
f
s
a
le
d
ata
ev
id
en
ce
tr
a
n
s
ac
t
io
n
o
f
GB
v
al
u
atio
n
.
Ho
w
e
v
er
,
t
h
e
ac
cu
r
ac
y
o
f
th
e
r
esu
lt
s
p
r
o
d
u
ce
d
b
y
t
h
e
ML
p
r
ed
ictio
n
m
o
d
el
is
h
ig
h
l
y
d
ep
en
d
en
t
o
n
m
a
n
y
f
ac
to
r
s
in
c
lu
d
in
g
t
h
e
alg
o
r
ith
m
s
h
y
p
er
-
p
ar
a
m
eter
s
tu
n
i
n
g
an
d
d
if
f
er
e
n
t
g
r
o
u
p
o
f
f
ea
tu
r
e
s
s
el
ec
tio
n
.
T
h
u
s
,
t
h
is
p
ap
er
is
w
r
i
tten
w
ith
t
h
e
ai
m
t
o
r
ep
o
r
t
th
e
d
esig
n
a
n
d
i
m
p
le
m
en
tatio
n
o
f
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
el
b
ased
o
n
a
u
to
h
y
p
er
-
p
a
r
a
m
eter
s
t
u
n
i
n
g
an
d
d
if
f
er
e
n
t g
r
o
u
p
s
o
f
f
ea
tu
r
e
s
el
ec
tio
n
.
T
h
e
co
n
tr
ib
u
tio
n
o
f
t
h
is
p
ap
e
r
is
t
w
o
-
f
o
ld
.
Firs
tl
y
,
it
i
n
tr
o
d
u
ce
s
t
h
e
d
esi
g
n
an
d
i
m
p
le
m
en
tatio
n
o
f
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
w
i
th
au
to
h
y
p
er
-
p
ar
a
m
e
ter
tu
n
i
n
g
.
I
n
th
e
m
e
th
o
d
o
lo
g
y
p
ar
t,
th
i
s
p
ap
er
p
r
o
v
id
es
th
e
tech
n
iq
u
e
o
f
a
u
to
h
y
p
er
-
p
ar
am
eter
tu
n
i
n
g
b
y
u
s
in
g
b
est
es
ti
m
ato
r
f
u
n
ctio
n
p
r
o
v
id
ed
b
y
P
h
yto
n
S
cikit
-
Lea
r
n
lib
r
ar
y
.
Seco
n
d
l
y
,
it
p
r
esen
ts
h
o
w
GB
d
eter
m
i
n
an
t
a
f
f
ec
ts
t
h
e
m
ac
h
in
e
lear
n
in
g
p
er
f
o
r
m
a
n
ce
in
p
r
ed
ictin
g
th
e
p
r
ice
o
f
b
u
ild
in
g
b
ased
o
n
r
ea
l
d
ataset
o
f
Ku
ala
L
u
m
p
u
r
d
is
tr
ict
in
Ma
la
y
s
ia
T
h
e
s
tr
u
ctu
r
e
of
t
h
is
p
ap
er
is
as
f
o
llo
w
s
.
S
e
c
t
i
o
n
I
I
f
o
c
u
s
e
s
o
n
t
h
e
b
ac
k
g
r
o
u
n
d
o
f
t
h
e
s
t
u
d
y
r
el
ated
to
th
e
ML
i
n
r
ea
l
p
r
ed
ictio
n
o
f
r
ea
l
estate
a
n
d
ML
al
g
o
r
ith
m
s
.
Sectio
n
I
I
I
d
escr
ib
es
th
e
r
ese
ar
ch
m
et
h
o
d
o
lo
g
y
f
o
llo
w
ed
b
y
t
h
e
d
is
c
u
s
s
io
n
o
f
th
e
r
esu
lt
in
s
ec
tio
n
I
V.
T
h
e
co
n
clu
d
i
n
g
r
e
m
ar
k
i
s
w
r
i
tten
i
n
th
e
last
s
ec
tio
n
.
2.
B
ACK
G
RO
UND
O
F
T
H
E
S
T
UDY
2
.
1
.
M
a
chine le
a
rning
f
o
r
re
a
l e
s
t
a
t
e
predict
io
n
A
cc
u
r
ate
ev
al
u
atio
n
o
f
p
r
o
p
er
t
y
p
r
ice
is
cr
u
cial
f
o
r
r
ea
l
estate,
th
e
s
to
ck
m
ar
k
e
t,
t
ax
s
ec
to
r
,
th
e
ec
o
n
o
m
y
a
n
d
th
e
p
o
w
er
o
f
p
u
r
ch
a
s
er
s
[
1
8
]
.
T
h
e
co
n
v
en
tio
n
al
m
et
h
o
d
is
li
m
ited
to
t
h
e
s
co
p
e
o
f
cu
r
r
e
n
t
s
y
s
te
m
s
d
ata
th
at
n
ee
d
s
to
b
e
tak
en
i
n
to
ac
co
u
n
t.
No
r
m
all
y
,
p
r
ed
ictin
g
t
h
e
p
r
ice
o
f
p
r
o
p
er
ty
i
s
o
f
te
n
d
o
n
e
th
r
o
u
g
h
b
asic
co
m
p
ar
ati
v
e
m
ar
k
et
an
a
l
y
s
is
as
w
e
ll
as
s
i
m
ilar
r
ea
l
estate
in
th
e
s
a
m
e
a
r
ea
to
p
r
o
v
id
e
an
ap
p
r
o
x
im
a
te
p
r
ice
f
o
r
a
p
ar
tic
u
lar
p
r
o
p
er
ty
[
1
9
]
.
B
u
t
in
GB
co
n
tex
t,
th
e
o
th
er
f
ac
to
r
s
t
h
a
t
ca
n
co
n
tr
ib
u
te
o
r
g
iv
e
p
o
s
iti
v
e
i
m
p
ac
t
o
r
ad
d
e
d
v
alu
e
s
to
th
e
GB
p
r
ice
s
h
o
u
ld
also
b
e
co
n
s
id
er
ed
to
p
r
o
d
u
ce
an
ac
cu
r
ate
r
esu
lt
in
t
h
e
p
r
ice
a
n
d
to
r
e
f
lect
th
e
cu
r
r
en
t
m
ar
k
et
v
al
u
e
[
2
0
]
.
T
h
is
w
ill
o
n
l
y
h
ap
p
en
i
f
th
e
v
alu
er
co
n
s
id
er
s
t
h
e
h
is
to
r
ical
f
ac
to
r
s
in
p
r
ed
i
ctin
g
th
e
p
r
ice
o
f
th
e
GB
.
M
L
is
s
ee
n
to
h
a
v
e
th
e
p
o
ten
tia
l
in
co
n
s
id
er
in
g
th
o
s
e
f
ac
to
r
s
an
d
p
r
o
b
lem
s
[
1
4
]
.
T
h
e
co
m
m
o
n
ML
m
o
d
elli
n
g
t
ec
h
n
iq
u
es
th
at
ar
e
alr
ea
d
y
b
ein
g
i
m
p
le
m
e
n
ted
i
n
r
ea
l
estate
p
r
o
b
lem
s
ar
e
L
i
n
ea
r
R
eg
r
es
s
io
n
[
2
1
-
23]
,
Dec
is
io
n
T
r
ee
[
2
4
-
27]
,
R
an
d
o
m
Fo
r
est
[
2
1
,
2
8
-
29]
,
R
id
g
e
R
eg
r
es
s
io
n
[
3
0
]
an
d
L
a
s
s
o
R
eg
r
e
s
s
io
n
[
2
4
,
3
1
]
.
T
h
e
f
u
n
ctio
n
o
f
all
t
h
ese
alg
o
r
ith
m
s
i
s
to
p
r
ed
ict
t
h
e
r
ea
l e
s
t
ate
d
ataset
a
n
d
th
e
r
esear
ch
er
s
test
all
t
h
e
s
e
alg
o
r
i
th
m
s
i
n
o
r
d
er
to
p
r
ed
ict
th
e
g
r
ee
n
b
u
ild
i
n
g
p
r
ices.
2
.
2
.
M
a
chine le
a
rning
a
lg
o
ri
t
h
m
T
h
er
e
ar
e
f
iv
e
(
5
)
ML
alg
o
r
ith
m
s
t
h
at
ar
e
u
s
ed
in
t
h
i
s
s
t
u
d
y
n
a
m
el
y
L
i
n
ea
r
R
e
g
r
ess
io
n
,
Dec
is
io
n
T
r
ee
,
R
an
d
o
m
Fo
r
est,
R
id
g
e
an
d
L
a
s
s
o
alg
o
r
it
h
m
s
.
L
i
n
ea
r
R
e
g
r
ess
io
n
(
L
R
)
is
o
n
e
o
f
th
e
m
o
s
t
w
ell
-
u
n
d
er
s
to
o
d
an
d
w
el
l
-
k
n
o
w
n
al
g
o
r
ith
m
s
i
n
M
L
an
d
s
tatis
t
ics.
I
t
is
a
ls
o
a
p
r
ed
ictiv
e
m
o
d
el
th
at
m
ai
n
l
y
co
n
ce
r
n
s
in
m
i
n
i
m
is
i
n
g
th
e
er
r
o
r
an
d
to
en
s
u
r
e
o
r
to
m
ak
e
th
e
m
o
s
t
ac
cu
r
ate
a
n
d
p
o
s
s
i
b
le
p
r
ed
ictio
n
in
ex
p
lain
in
g
th
e
d
ataset
ab
ilit
y
.
T
h
e
r
ep
r
esen
tatio
n
o
f
L
R
alg
o
r
ith
m
i
s
an
eq
u
atio
n
t
h
at
ex
p
lain
s
an
d
d
escr
ib
es
a
li
n
e
w
h
ic
h
en
s
u
r
e
s
th
e
b
es
t
f
i
ts
o
f
th
e
r
elatio
n
s
h
ip
b
et
w
e
e
n
th
e
o
u
tp
u
t
v
ar
iab
le
s
(
y
)
a
n
d
in
p
u
t
v
ar
iab
les
(
x
)
,
b
y
f
i
n
d
in
g
th
e
e
x
ac
t
w
ei
g
h
tin
g
f
o
r
th
e
in
p
u
t
v
ar
iab
le
th
at
is
ca
l
led
co
ef
f
icie
n
t (
B
)
[
3
2
]
.
T
h
e
f
o
r
m
u
la
i
n
(
1
)
r
ep
r
ese
n
ti
n
g
t
h
e
L
i
n
ea
r
R
eg
r
e
s
s
io
n
al
g
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
Ma
ch
in
e
lea
r
n
in
g
b
u
ild
in
g
p
r
ice
p
r
ed
ictio
n
w
ith
g
r
ee
n
b
u
ild
in
g
d
etermin
a
n
t
(
Th
u
r
a
iya
Mo
h
d
)
381
=
0
+
1
∗
(
1
)
I
n
th
i
s
f
o
r
m
u
la,
Y
is
t
h
e
d
ep
en
d
en
t
v
ar
iab
le
(
DV)
b
y
t
h
e
g
i
v
en
i
n
p
u
t
(
x
)
w
h
ich
is
t
h
e
i
n
d
ep
en
d
en
t
v
ar
iab
le
(
I
V)
.
T
h
e
m
ai
n
g
o
al
o
f
th
e
L
i
n
ea
r
R
eg
r
es
s
io
n
al
g
o
r
ith
m
i
s
to
f
in
d
th
e
v
al
u
e
f
o
r
th
e
co
ef
f
icien
ts
0
an
d
1
[
2
1
-
2
2
,
2
5
]
.
Du
e
to
th
e
s
i
m
p
licit
y
o
f
alg
o
r
ith
m
,
L
i
n
ea
r
R
eg
r
ess
io
n
h
as
b
ee
n
co
m
m
o
n
l
y
u
s
ed
in
r
ea
l
estate
p
r
ed
ictio
n
p
r
o
b
lem
[
1
3
-
1
5
]
.
Dec
is
io
n
T
r
ee
(
D
T
)
is
an
o
th
er
c
o
m
m
o
n
m
o
d
e
l
u
s
ed
to
s
o
lv
e
r
eg
r
es
s
io
n
an
d
class
i
f
icatio
n
p
r
o
b
lem
[
3
3
]
.
T
h
e
alg
o
r
it
h
m
p
r
o
d
u
ce
s
a
tr
ee
s
tr
u
ct
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r
e
th
at
in
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d
es
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r
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o
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d
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r
an
ch
es.
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ac
h
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ter
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al
n
o
d
e
s
ta
n
d
s
f
o
r
a
test
o
n
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attr
ib
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te
,
each
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r
an
ch
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en
o
te
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th
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o
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tco
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e
o
f
a
test
,
w
h
ic
h
is
ca
l
led
a
d
ec
is
io
n
n
o
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e
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ea
ch
leaf
n
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e
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o
ld
s
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class
lab
el
w
h
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ch
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ca
lled
a
ter
m
in
a
l
n
o
d
e.
T
h
e
to
p
m
o
s
t
o
f
th
e
n
o
d
e
in
th
e
tr
ee
is
ca
l
led
a
r
o
o
t n
o
d
e
[
3
3
-
34]
as p
r
esen
ted
in
Fig
u
r
e
1.
F
ig
u
r
e
1
.
Dec
is
io
n
tr
ee
s
tr
u
ctu
r
e
Ho
w
e
v
er
,
p
r
ev
io
u
s
r
esear
ch
s
h
o
w
ed
th
e
d
esig
n
s
w
h
ic
h
in
d
icate
th
at
th
e
DT
alg
o
r
ith
m
ca
n
p
r
o
v
id
e
a
h
ig
h
er
ac
cu
r
ac
y
t
o
d
ataset,
co
m
p
ar
e
d
to
th
e
o
th
er
alg
o
r
it
h
m
li
k
e
L
a
s
s
o
[
2
4
]
.
DT
h
as
n
o
p
r
o
b
lem
s
in
ap
p
r
o
x
i
m
ati
n
g
th
e
lin
ea
r
r
elat
io
n
s
h
ip
s
b
ased
o
n
I
n
d
ep
en
d
e
n
t
Var
ia
b
le
an
d
Dep
en
d
e
n
t
Var
iab
le
f
ac
to
r
s
[
2
5
-
26]
.
I
t is
g
o
o
d
to
p
er
f
o
r
m
th
e
al
g
o
r
ith
m
w
h
en
it c
o
m
es to
p
r
ed
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n
.
T
h
e
R
an
d
o
m
Fo
r
est
(
R
F)
is
an
a
d
v
a
n
ce
d
tr
ee
s
tr
u
ct
u
r
es
f
r
o
m
t
h
e
DT
,
[
3
5
-
38]
.
I
t
is
a
t
y
p
e
o
f
en
s
e
m
b
led
ML
m
o
d
el
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lled
B
o
o
ts
tr
ap
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ag
g
in
g
o
r
Ag
g
r
eg
atio
n
.
T
h
e
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o
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ts
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ap
is
a
p
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er
f
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l
s
tat
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m
et
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o
d
f
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m
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g
a
q
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a
n
tit
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o
m
a
d
ata
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m
p
le
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ch
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t
h
e
m
ea
n
.
R
F
m
o
d
el
w
ill
tak
e
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lo
t
o
f
d
ata
s
a
m
p
les,
ca
lc
u
late
t
h
e
m
ea
n
,
th
en
a
v
er
ag
e
al
l
o
f
t
h
e
m
ea
n
v
alu
es
to
g
iv
e
a
b
etter
es
ti
m
a
ti
o
n
r
esu
lt
o
f
t
h
e
tr
u
e
m
ea
n
v
al
u
e
[
3
9
]
.
Sev
er
al
r
ese
ar
ch
h
av
e
d
e
m
o
n
s
tr
ated
th
a
t
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m
o
s
tl
y
o
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tp
er
f
o
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m
s
m
a
n
y
o
th
er
alg
o
r
ith
m
i
n
d
ea
lin
g
w
i
th
p
r
o
b
le
m
r
elate
d
to
p
r
o
p
er
ty
p
r
ice
[
2
1
,
28
-
29]
.
T
h
e
R
id
g
e
al
g
o
r
ith
m
is
o
n
e
o
f
ML
m
o
d
els
th
at
is
u
s
ed
f
o
r
a
n
al
y
s
i
n
g
m
u
lt
ip
le
r
eg
r
es
s
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d
ataset
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h
at
s
u
f
f
er
s
f
r
o
m
m
u
ltico
lli
n
ea
r
it
y
.
Mu
ltico
lli
n
ea
r
it
y
i
s
also
ca
lle
d
as
co
llin
ea
r
it
y
t
h
at
r
ef
er
s
to
a
p
o
s
itio
n
in
w
h
ich
t
w
o
o
r
m
o
r
e
in
f
o
r
m
a
tiv
e
v
ar
ia
b
les
in
a
m
u
ltip
le
r
eg
r
es
s
io
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r
e
h
ig
h
l
y
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elate
d
.
E
v
en
t
h
o
u
g
h
,
R
id
g
e
R
e
g
r
es
s
io
n
alg
o
r
ith
m
i
s
ad
d
ed
in
th
at
p
r
o
b
le
m
,
a
d
eg
r
ee
o
f
b
ias
to
th
e
r
eg
r
ess
io
n
ca
n
s
till
b
e
esti
m
ate
d
.
R
id
g
e
R
e
g
r
ess
io
n
is
a
m
o
d
el
th
a
t
en
f
o
r
ce
s
th
e
c
o
ef
f
icie
n
t
to
b
e
lo
w
er
b
u
t
it
d
o
es
n
o
t
en
f
o
r
ce
th
e
m
to
b
e
ze
r
o
,
as
it
w
ill
n
o
t
g
e
t
r
id
o
f
ir
r
elev
an
t
f
ea
t
u
r
e
b
u
t
r
ath
er
m
i
n
i
m
is
in
g
th
e
ir
i
m
p
ac
t
o
n
th
e
tr
ain
i
n
g
m
o
d
el
[
4
0
]
.
T
o
av
o
id
o
v
er
f
itti
n
g
,
R
id
g
e
R
e
g
r
es
s
io
n
al
g
o
r
ith
m
p
er
f
o
r
m
s
L
2
r
eg
u
lar
is
a
tio
n
s
t
ated
in
th
e
f
o
r
m
u
la.
Me
an
w
h
ile,
L
as
s
o
alg
o
r
ith
m
u
s
e
s
L
1
r
eg
u
lar
is
at
io
n
[
4
1
]
. E
q
u
atio
n
(
2
)
d
en
o
tes R
id
g
e
al
g
o
r
ith
m
.
=
+
(
2
)
I
n
th
i
s
f
o
r
m
u
la,
Y
d
en
o
tes
f
o
r
DV,
X
as
I
V
an
d
B
r
ep
r
es
en
ts
t
h
e
r
eg
r
es
s
io
n
o
f
co
ef
f
ic
ien
t
to
b
e
p
r
ed
icted
[
4
0
]
.
T
h
e
r
ep
r
esen
ts
th
e
r
esid
u
al
er
r
o
r
s
.
T
h
er
e
ar
e
s
o
m
e
r
e
s
ea
r
ch
w
h
ic
h
p
r
o
v
e
th
at
R
id
g
e
R
eg
r
es
s
io
n
ca
n
b
e
less
p
er
f
o
r
m
ed
co
m
p
ar
ed
to
L
in
ea
r
R
e
g
r
ess
io
n
alt
h
o
u
g
h
th
e
R
id
g
e
R
e
g
r
ess
io
n
is
d
esig
n
ed
to
h
an
d
le
m
u
ltico
lli
n
ea
r
it
y
in
m
o
d
ellin
g
h
o
u
s
e
p
r
ice
[
3
0
]
.
I
n
th
e
o
t
h
er
s
t
u
d
y
o
n
h
o
u
s
e
p
r
ice
p
r
ed
ictio
n
,
L
as
s
o
R
eg
r
e
s
s
io
n
h
as
o
u
tp
er
f
o
r
m
ed
R
id
g
e
alg
o
r
it
h
m
i
n
h
a
n
d
lin
g
m
u
ltico
lli
n
ea
r
it
y
.
Fu
r
t
h
er
m
o
r
e,
i
n
r
ea
l
estate
v
alu
e
p
r
ed
ictio
n
u
s
i
n
g
m
u
ltip
l
e
alg
o
r
it
h
m
,
L
a
s
s
o
r
e
g
r
ess
io
n
alg
o
r
ith
m
s
ee
m
s
to
o
v
er
f
it
t
h
e
ir
m
o
d
el
d
ataset
b
y
u
s
i
n
g
R
id
g
e
R
e
g
r
ess
io
n
alg
o
r
i
th
m
s
[
4
2
]
.
L
as
s
o
r
eg
r
es
s
io
n
a
lg
o
r
it
h
m
s
t
an
d
s
f
o
r
L
ea
s
t
A
b
s
o
l
u
te
Sele
ctio
n
a
n
d
Sh
r
i
n
k
ag
e
Op
er
ato
r
an
d
it
ca
n
p
er
f
o
r
m
b
o
th
tas
k
s
w
h
ic
h
ar
e
f
ea
tu
r
e
s
elec
tio
n
an
d
r
eg
u
lar
is
atio
n
.
T
h
e
o
n
l
y
d
if
f
er
en
ce
o
f
L
as
s
o
alg
o
r
ith
m
f
r
o
m
R
id
g
e
R
e
g
r
es
s
io
n
alg
o
r
i
th
m
i
s
t
h
at
th
e
r
eg
u
lar
is
atio
n
ter
m
i
s
i
n
ab
s
o
l
u
te
v
al
u
e.
I
t
is
s
et
to
r
estra
in
t
t
h
e
s
u
m
o
f
t
h
e
ab
s
o
lu
te
v
a
lu
e
s
o
f
th
e
m
o
d
el
p
ar
a
m
eter
s
w
h
er
e
th
e
s
u
m
m
u
s
t
b
e
less
t
h
an
a
f
i
x
ed
v
a
lu
e
[
4
3
-
44]
.
B
esid
es
th
a
t,
L
as
s
o
alg
o
r
it
h
m
is
b
ei
n
g
ap
p
lied
in
a
s
h
r
i
n
k
i
n
g
(
r
eg
u
lar
is
atio
n
)
p
r
o
ce
s
s
wh
er
e
it
p
e
n
alize
s
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
3
,
Sep
te
m
b
er
20
20
:
3
7
9
–
386
382
co
ef
f
icie
n
t
s
o
f
th
e
r
eg
r
es
s
io
n
v
ar
iab
le
s
s
h
r
i
n
k
i
n
g
s
o
m
e
o
f
th
e
m
to
ze
r
o
if
th
e
y
ar
e
n
o
t
r
elev
an
t.
I
n
d
ee
d
,
th
is
p
r
o
ce
s
s
i
s
b
ein
g
ap
p
lied
to
m
i
n
i
m
i
s
e
th
e
p
r
ed
ictio
n
er
r
o
r
.
R
esear
ch
in
[
2
4
]
h
as
d
e
m
o
n
s
tr
ated
th
e
p
o
t
en
t
ial
o
f
L
as
s
o
alg
o
r
ith
m
to
p
r
o
d
u
ce
h
i
g
h
er
ac
c
u
r
ac
y
th
a
n
L
i
n
ea
r
r
eg
r
es
s
io
n
a
n
d
d
ec
is
io
n
tr
ee
w
it
h
i
n
t
h
e
s
co
p
e
o
f
s
t
u
d
y
.
T
h
e
al
g
o
r
ith
m
w
as
e
m
p
lo
y
ed
in
p
r
e
d
ictin
g
th
e
h
o
u
s
e
p
r
ice
in
Am
es
,
I
o
w
a
i
n
Un
ited
State
u
s
in
g
r
ea
l
e
s
tat
e
d
ata
f
r
o
m
2
0
1
6
to
2
0
2
0
an
d
it
w
as
f
o
u
n
d
t
h
a
t
L
as
s
o
alg
o
r
it
h
m
o
u
tp
er
f
o
r
m
ed
R
id
g
e
al
g
o
r
ith
m
i
n
th
i
s
ca
s
e
[
3
0
]
.
T
h
e
r
esear
ch
er
s
also
m
en
t
io
n
ed
th
at
L
as
s
o
is
v
er
y
u
s
e
f
u
l f
o
r
f
ea
t
u
r
es se
lecti
o
n
an
d
to
eli
m
i
n
ate
a
n
y
u
s
ele
s
s
f
ea
t
u
r
es.
3.
M
E
T
H
O
DO
L
O
G
Y
3
.
1
.
Da
t
a
s
et
T
h
e
d
ataset
is
a
co
llectio
n
o
f
h
o
u
s
in
g
p
r
ices
i
n
2
0
1
8
w
it
h
d
eter
m
i
n
a
n
ts
t
h
at
i
n
c
lu
d
es
GB
.
As
t
h
is
p
ap
er
u
s
es
m
ac
h
i
n
e
le
ar
n
in
g
p
r
ed
ictio
n
,
t
h
ese
v
ar
ia
b
les
ar
e
ca
lled
f
ea
t
u
r
es.
T
ab
le
1
s
h
o
w
s
th
e
s
et
o
f
f
ea
t
u
r
es
to
d
ev
e
lo
p
th
e
m
ac
h
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I
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No
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3
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Sep
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b
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20
20
:
3
7
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–
386
384
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[1
]
A
.
Ke
y
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T
.
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c
h
,
a
n
d
J.
A
.
M
e
d
,
“
A
rti
f
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l
In
telli
g
e
n
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e
&
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e
d
ica
l
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sis,”
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c
h
.
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.
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p
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M
e
d
.
S
c
i.
(
S
J
AM
S
)
Arti
f.
In
tell.
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e
d
.
Di
a
g
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o
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o
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p
p
.
4
9
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[2
]
J.
B.
O.
M
it
c
h
e
ll
B.
O.,
“
M
a
c
h
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L
e
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rn
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e
th
o
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Ch
e
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ter
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.
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.
[3
]
K.
H.
S
a
d
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.
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,
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.
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.
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a
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s,”
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t.
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.
E
n
g
.
Ad
v
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.
[4
]
O.
S
.
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,
S
.
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g
a
d
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v
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n
,
a
n
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H.
A
.
H.
M
a
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,
“
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rd
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r
a
u
d
De
tec
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in
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s
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ta
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in
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n
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T
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iq
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,
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.
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mm
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,
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0
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.
[5
]
N.
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M
.
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Ra
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a
n
d
S
.
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,
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ro
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to
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5
.
[6
]
M
.
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Ra
d
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,
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.
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B.
Ka
sh
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.
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.
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p
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5
.
[7
]
M
.
O.
A
ji
b
o
la
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d
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O.
Aw
o
d
iran
,
“
Eff
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c
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o
f
In
f
ra
stru
c
tu
re
o
n
P
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p
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rty
V
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lu
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s
in
Un
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,
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a
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,
Nig
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In
t.
J
.
Eco
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a
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.
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b
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iz,
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,
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ip
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[9
]
L
.
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b
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,
W
.
N.
A
.
W
.
A
.
Ra
s
id
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.
M
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,
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Ro
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V
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:
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Re
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w
,
”
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t.
J
.
Aca
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.
Res
.
Bu
s.
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o
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.
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0
]
R.
A
.
M
a
ji
d
,
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I
m
p
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o
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Gre
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0
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
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8938
Ma
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385
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3
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.
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.
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t,
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.
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,
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4
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.
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o
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rn
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n
d
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Af
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,
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l
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it
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1
1
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4
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0
1
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.
[1
5
]
D.
K.
Ch
o
u
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y
,
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.
Ku
m
a
r,
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.
T
rip
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d
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.
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r,
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.
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[1
6
]
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L
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.
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d
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.
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m
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,
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A
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7
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.
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ra
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8
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rtzi,
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.
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lo
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.
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tzic
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risto
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l:
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e
th
o
d
s,”
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.
Pro
p
.
I
n
v
e
st.
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n
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.
,
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l.
2
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3
8
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4
0
1
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2
0
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.
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9
]
M
.
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m
m
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ro
w
,
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h
e
o
ry
f
o
r
Re
a
l
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lu
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n
:
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n
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lt
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.
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.
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0
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ll
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a
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rs ’
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v
.
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n
g
.
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i
.
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.
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o
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.
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1
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.
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.
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a
te In
v
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d
v
isin
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in
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,
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t.
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.
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g
.
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.
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2
]
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.
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m
o
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lo
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T
y
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li
s,
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P
.
Ba
k
a
s,
a
n
d
D.
Ha
d
ji
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it
sis,
“
A
c
c
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ra
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y
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n
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o
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e
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im
a
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rice
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o
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sid
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l
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p
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rtm
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ts
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o
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.
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-
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,
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8
.
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3
]
M
.
V
a
n
W
e
z
e
l
a
n
d
R.
P
o
th
a
rst,
“
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o
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ra
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d
o
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rici
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o
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e
ls,”
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n
o
m.
I
n
st.
Rep
.
EI
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0
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-
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,
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o
.
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a
y
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.
1
-
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0
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5
.
[2
4
]
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S
h
i
n
d
e
a
n
d
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G
a
w
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n
d
e
,
“
V
a
lu
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ti
o
n
o
f
Ho
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se
P
r
ice
s
Us
in
g
P
re
d
ictiv
e
T
e
c
h
n
iq
u
e
s,”
In
t.
J
.
Ad
v
.
E
lec
tro
n
.
Co
mp
u
t
.
S
c
i
.
IS
S
N2
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9
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-
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5
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l
.
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,
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3
4
-
4
0
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0
1
8
.
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5
]
M
.
V
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n
W
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z
e
l
a
n
d
R.
P
o
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a
rst,
“
Bo
o
sti
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e
A
c
c
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ra
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o
f
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d
o
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,
”
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n
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Rep
.
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2
0
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,
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l.
2
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o
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p
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-
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8
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2
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0
5
.
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6
]
Y.
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a
,
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Zh
a
n
g
,
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.
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ler,
a
n
d
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.
P
a
n
,
“
Esti
m
a
ti
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g
Ware
h
o
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se
Re
n
tal
P
rice
u
sin
g
M
a
c
h
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n
e
L
e
a
rn
in
g
Tec
h
n
iq
u
e
s,”
In
t.
J
.
C
o
mp
u
t.
C
o
mm
u
n
.
Co
n
tro
l
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S
N
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l.
1
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o
.
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,
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p
.
2
3
5
-
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,
2
0
1
8
.
[2
7
]
A
.
Ba
ld
o
m
in
o
s,
I.
Blan
c
o
,
A
.
J.
M
o
re
n
o
,
R.
Itu
rra
rte,
Ó.
Be
rn
á
rd
e
z
,
a
n
d
C.
Af
o
n
so
,
“
Id
e
n
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fy
in
g
Re
a
l
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o
r
tu
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it
ies
Us
in
g
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a
c
h
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n
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rn
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,
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p
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S
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i.
M
d
p
i
J
.
,
p
p
.
2
-
1
3
,
2
0
1
8
.
[2
8
]
W
.
Lee
,
N.
Ki
m
,
Y.
Ch
o
i,
Y.
S
.
Kim
,
a
n
d
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L
e
e
,
“
M
a
c
h
in
e
Lea
r
n
in
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b
a
se
d
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re
d
icti
o
n
o
f
th
e
V
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lu
e
o
f
Bu
il
d
in
g
s,”
KS
II
T
ra
n
s.
I
n
ter
n
e
t
In
f.
S
y
st.
,
v
o
l
.
1
2
,
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o
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8
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p
p
.
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9
6
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9
9
1
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2
0
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.
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9
]
M
.
A
.
V
a
ll
e
,
“
P
ro
p
e
rty
V
a
lu
a
ti
o
n
u
sin
g
M
a
c
h
in
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L
e
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rn
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A
lg
o
rit
h
m
s :
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S
tu
d
y
in
a
M
e
tro
p
o
li
ta
n
-
A
re
a
o
f
Ch
il
e
,
”
In
t.
C
o
n
f
.
M
o
d
e
l.
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im
u
l.
S
a
n
ti
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g
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il
e
,
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o
.
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a
y
2
0
1
7
,
p
p
.
1
-
1
3
3
,
2
0
1
6
.
[3
0
]
S
.
J.
X
i
n
a
n
d
K.
Kh
a
li
d
,
“
M
o
d
e
ll
in
g
Ho
u
se
P
rice
Us
in
g
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g
e
Re
g
re
ss
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n
d
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a
s
so
Re
g
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ss
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n
,
”
In
t.
J
.
En
g
.
T
e
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h
n
o
l
.
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l
.
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p
p
.
4
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8
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5
0
1
,
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0
1
8
.
[3
1
]
S
.
L
u
,
Z.
L
i,
Z.
Qin
,
X
.
Ya
n
g
,
R.
S
io
w
,
a
n
d
M
.
G
o
h
,
“
A
H
y
b
rid
Re
g
r
e
ss
io
n
T
e
c
h
n
iq
u
e
f
o
r
Ho
u
se
P
rice
s
P
re
d
ictio
n
,
”
Co
n
f.
Pa
p
.
I
n
st.
Hi
g
h
Per
fo
rm
.
Co
m
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u
t.
,
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st 2
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2
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A
.
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id
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.
Ho
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m
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a
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d
M
.
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p
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[3
3
]
W
.
L
o
h
,
“
Clas
sif
i
c
a
ti
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tree
s,”
W
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Da
ta
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Kn
o
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Disc
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.
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,
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p
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-
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4
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0
1
1
.
[3
4
]
H.
S
h
a
rm
a
a
n
d
S
.
Ku
m
a
r,
“
A
S
u
rv
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a
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f
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n
in
Da
ta
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in
in
g
,
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In
t.
J
.
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,
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4
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p
p
.
2
0
9
4
-
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0
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7
,
2
0
1
6
.
[3
5
]
J.
Iz
e
n
m
a
n
,
M
o
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rn
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lt
iva
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te
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ta
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e
s
,
v
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1
0
2
.
2
0
0
6
.
[3
6
]
J.
F
ra
n
k
li
n
,
“
T
h
e
El
e
m
e
n
ts
o
f
S
tatisti
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rn
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ta
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,
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a
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.
In
tell.
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l.
2
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o
.
2
,
p
p
.
8
3
-
8
5
,
2
0
0
5
.
[3
7
]
C.
S
tro
b
l
,
J.
M
a
ll
e
y
,
a
n
d
G
.
T
u
tz,
“
A
n
In
tro
d
u
c
ti
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to
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rsiv
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P
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:
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le,
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p
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d
Ch
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ra
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re
e
s,
Ba
g
g
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g
,
a
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d
Ra
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d
o
m
F
o
re
sts,”
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c
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M
e
th
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d
s
,
v
o
l.
1
4
,
n
o
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4
,
p
p
.
3
2
3
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4
8
,
2
0
0
9
.
[3
8
]
A
.
Cu
tl
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D.
R.
C
u
tl
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a
n
d
J.
R.
S
tev
e
n
s,
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Ra
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a
p
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ry
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0
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4
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1
,
p
.
1
5
7
.
[3
9
]
J.
A
li
,
R.
Kh
a
n
,
N.
A
h
m
a
d
,
a
n
d
I.
M
a
q
so
o
d
,
“
Ra
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m
F
o
re
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a
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d
De
c
isio
n
T
re
e
s,”
In
t.
J
.
C
o
m
p
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t
.
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.
9
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5
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p
p
.
2
7
2
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.
[4
0
]
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C.
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u
e
strin
,
“
Rid
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R
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in
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[4
1
]
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.
C.
B
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n
,
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Las
so
:
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sio
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s,”
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p
.
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a
th
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Un
iv.
Os
lo
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d
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il
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s
.
Dr
.
,
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6
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3
.
[4
2
]
R.
M
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la,
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.
Ja
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riv
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sta
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Re
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Estate
V
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sin
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u
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riate
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C
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En
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.
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[4
3
]
J.
M
.
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ira,
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.
Ba
sto
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F
.
d
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[4
4
]
V
.
F
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ti
,
“
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Us
in
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Amste
rd
a
m
,
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p
.
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-
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6
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0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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Vo
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9
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No
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3
,
Sep
te
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b
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20
20
:
3
7
9
–
386
386
B
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RAP
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AUTH
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in
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t
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Ca
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p
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S
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in
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UiT
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Bu
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M
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ro
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d
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Bh
d
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1
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rsity
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rsity
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k
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g
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M
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R
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(Ui
T
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),
S
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ri
Isk
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n
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r,
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ra
k
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M
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lay
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in
2
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s.
In
UiT
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sh
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h
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s
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m
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is c
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rre
n
tl
y
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c
ti
v
e
w
it
h
th
re
e
re
se
a
rc
h
g
ra
n
ts.
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