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T
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n
th
is
p
ap
er
,
th
e
Har
r
is
Ha
w
k
s
o
p
ti
m
izatio
n
(
HHO)
al
g
o
r
ith
m
w
ill
b
e
u
s
ed
to
f
i
n
d
t
h
e
k
e
y
f
ea
t
u
r
es
t
h
at
ca
n
b
e
u
s
ed
to
d
i
s
tin
g
u
is
h
s
p
a
m
f
r
o
m
h
a
m
e
m
a
ils
[
9
]
,
[
1
2
]
.
T
h
e
w
id
el
y
k
n
o
wn
I
SC
X
-
UR
L
2
0
1
6
s
p
a
m
d
ataset
w
i
ll
b
e
u
s
ed
as
a
b
en
ch
m
ar
k
f
o
r
e
m
ail
d
ata
[
1
3
]
.
T
h
e
ef
f
icie
n
c
y
o
f
t
h
e
r
es
u
lti
n
g
s
u
b
s
et
w
ill
b
e
test
ed
u
s
i
n
g
th
r
ee
co
m
m
o
n
s
u
p
er
v
is
ed
lear
n
i
n
g
tec
h
n
iq
u
es,
n
a
m
e
l
y
DT
,
NB
,
an
d
A
d
aB
o
o
s
t.
I
n
ad
d
itio
n
,
t
h
e
h
y
p
er
p
ar
a
m
eter
s
o
f
th
e
s
e
th
r
ee
tech
n
iq
u
e
s
w
ill
b
e
tailo
r
e
d
to
ac
h
iev
e
th
e
b
est
p
er
f
o
r
m
an
ce
a
n
d
s
u
it
th
e
p
r
o
b
lem
at
h
a
n
d
.
Sev
er
al
w
o
r
k
s
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
s
p
am
d
etec
tio
n
.
R
at
h
i
an
d
P
ar
ee
k
[
1
4
]
in
v
es
tig
ate
a
v
ar
iet
y
o
f
ML
alg
o
r
it
h
m
s
f
o
r
th
e
s
p
a
m
d
ata
s
et,
k
ee
p
in
g
in
m
i
n
d
th
e
u
lt
i
m
ate
o
b
j
ec
tiv
e
o
f
d
eter
m
i
n
i
n
g
th
e
m
o
s
t
ef
f
ec
ti
v
e
ML
al
g
o
r
ith
m
f
o
r
s
p
a
m
e
m
ai
l
ca
teg
o
r
izatio
n
.
R
esear
c
h
er
s
ev
alu
a
ted
th
e
ef
f
ec
ti
v
e
n
es
s
o
f
s
e
v
er
al
d
i
f
f
er
en
t
alg
o
r
ith
m
s
,
b
o
th
w
i
th
a
n
d
w
i
t
h
o
u
t
th
e
u
s
e
o
f
f
ea
t
u
r
e
s
elec
tio
n
al
g
o
r
ith
m
s
.
T
h
e
in
it
ial
p
h
a
s
e
o
f
t
h
e
p
r
o
ce
s
s
w
a
s
test
i
n
g
all
al
g
o
r
ith
m
s
o
n
th
e
en
tire
d
ataset
w
it
h
o
u
t
s
elec
t
in
g
a
n
y
f
ea
t
u
r
es.
Af
ter
th
a
t,
t
h
e
B
est
-
First
f
ea
t
u
r
e
s
elec
tio
n
tec
h
n
iq
u
e
w
as
u
til
iz
ed
to
id
en
tify
t
h
e
d
esire
d
f
ea
t
u
r
es,
an
d
af
ter
w
ar
d
s
,
s
ev
er
al
d
if
f
er
e
n
t
clas
s
i
f
ier
s
w
er
e
u
s
ed
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ated
th
at
ap
p
ly
i
n
g
th
e
B
est
-
First
f
ea
t
u
r
e
s
elec
tio
n
m
et
h
o
d
in
cr
ea
s
es
ac
cu
r
ac
y
.
T
h
e
r
an
d
o
m
f
o
r
est
alg
o
r
ith
m
atta
in
ed
t
h
e
h
i
g
h
e
s
t
ac
cu
r
ac
y
o
f
9
9
.
7
2
%
a
m
o
n
g
all
th
e
alg
o
r
it
h
m
s
ap
p
lied
.
I
n
co
m
p
ar
is
o
n
,
t
h
e
N
B
alg
o
r
ith
m
h
as
at
tain
ed
t
h
e
l
o
w
est ac
c
u
r
ac
y
o
f
7
8
.
9
3
%.
R
av
i
et
a
l
.
[
1
5
]
s
tu
d
ied
t
h
e
d
et
ec
tio
n
o
f
m
a
licio
u
s
u
n
i
f
o
r
m
r
eso
u
r
ce
lo
ca
to
r
s
(
UR
L
s
)
b
y
co
n
d
u
cti
n
g
a
co
m
p
ar
ati
v
e
e
x
a
m
in
a
tio
n
o
f
s
ev
er
al
d
i
f
f
er
en
t
d
ee
p
lear
n
i
n
g
-
b
ased
c
h
ar
ac
ter
-
le
v
el
e
m
b
ed
d
in
g
(
D
L
-
C
L
E
)
m
o
d
el
s
.
C
o
n
ce
r
n
in
g
ac
cu
r
ac
y
,
ev
er
y
DL
ar
ch
itect
u
r
e
p
o
s
s
ess
es
s
o
m
e
d
eg
r
ee
o
f
d
if
f
er
en
tia
tio
n
.
On
th
e
o
th
er
h
an
d
,
t
h
e
m
o
d
els
t
h
at
w
er
e
p
u
t
t
h
r
o
u
g
h
t
h
eir
p
ac
es
p
er
f
o
r
m
ed
w
ell
an
d
attai
n
ed
a
d
etec
tio
n
r
ate
o
f
b
et
w
ee
n
9
3
an
d
9
8
%
f
o
r
m
al
icio
u
s
UR
L
s
,
w
i
th
a
f
al
s
e
p
o
s
itiv
e
r
ate
o
f
0
.
0
0
1
.
T
h
is
s
u
g
g
e
s
ts
t
h
at
e
v
en
i
f
t
h
e
D
L
-
C
L
E
m
o
d
el
s
ca
n
id
en
tify
9
7
0
m
a
li
cio
u
s
U
R
L
s
,
th
e
y
w
i
ll
o
n
l
y
c
l
ass
i
f
y
o
n
e
n
o
n
-
m
alicio
u
s
UR
L
a
s
m
alicio
u
s
.
T
h
e
DL
-
C
L
E
m
o
d
els
p
er
f
o
r
m
ed
b
etter
th
an
t
h
e
o
th
er
m
o
d
el
s
ac
r
o
s
s
all
test
ca
s
e
s
.
All
D
L
-
C
L
E
m
o
d
els
ca
n
d
ea
l
w
it
h
m
alicio
u
s
U
R
L
d
r
if
ti
n
g
a
n
d
p
r
o
v
id
e
a
r
eliab
le
s
o
lu
tio
n
i
n
an
u
n
r
eliab
le
e
n
v
ir
o
n
m
e
n
t.
L
e
et
a
l
.
[
1
6
]
s
u
g
g
e
s
t
an
en
d
-
to
-
en
d
DL
f
r
a
m
e
w
o
r
k
th
a
t
th
e
y
ter
m
U
R
L
Net.
T
h
is
f
r
a
m
e
wo
r
k
aim
s
to
tr
ain
n
o
n
l
in
ea
r
UR
L
e
m
b
ed
d
i
n
g
an
d
d
etec
t
f
r
au
d
u
len
t
UR
L
s
at
th
e
UR
L
le
v
el.
A
cc
o
r
d
in
g
to
th
is
m
e
th
o
d
,
th
e
m
o
d
el
ca
n
ca
p
t
u
r
e
m
u
ltip
le
s
o
r
ts
o
f
s
e
m
a
n
tic
in
f
o
r
m
at
io
n
,
w
h
ic
h
w
a
s
n
o
t
ac
h
iev
ab
le
w
it
h
th
e
p
r
ev
io
u
s
l
y
av
ailab
le
m
o
d
el
s
.
I
n
ad
d
itio
n
,
ad
v
an
ce
d
w
o
r
d
e
m
b
ed
d
in
g
s
ar
e
ad
v
o
ca
ted
as a
p
o
ten
tial
s
o
l
u
tio
n
to
t
h
e
is
s
u
e
o
f
an
ex
c
e
s
s
i
v
e
n
u
m
b
er
o
f
u
n
co
m
m
o
n
w
o
r
d
s
b
ein
g
n
o
ted
.
E
x
ten
s
i
v
e
e
x
p
er
i
m
e
n
ts
ar
e
ca
r
r
ie
d
o
u
t
o
n
a
m
ass
iv
e
d
ataset,
d
em
o
n
s
tr
ati
n
g
a
s
i
g
n
i
f
ican
t
p
er
f
o
r
m
an
ce
i
m
p
r
o
v
e
m
en
t
o
v
er
ex
i
s
ti
n
g
ap
p
r
o
ac
h
es.
I
n
ad
d
itio
n
,
a
co
m
p
o
n
e
n
t a
n
al
y
s
i
s
s
t
u
d
y
is
c
ar
r
ied
o
u
t to
ass
ess
t
h
e
o
v
er
all
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
UR
L
Ne
t c
o
m
p
o
n
en
t
s
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
is
cu
s
s
e
s
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
d
etec
tin
g
s
p
a
m
e
m
ai
ls
.
First,
th
e
I
S
C
X
-
U
R
L
2
0
1
6
d
ataset
w
ill
b
e
p
r
esen
ted
.
T
h
en
,
th
e
HH
O
A
l
g
o
r
ith
m
u
s
ed
f
o
r
f
ea
tu
r
e
s
elec
tio
n
w
ill
b
e
ad
d
r
ess
ed
.
Fin
all
y
,
th
e
a
lg
o
r
ith
m
s
u
s
ed
f
o
r
th
e
clas
s
if
icatio
n
p
r
o
ce
s
s
w
ill b
e
elab
o
r
ated
o
n
.
2
.
1
.
I
SCX
-
URL2
0
1
6
s
pa
m
d
a
t
a
s
et
T
h
e
d
ata
in
s
id
e
th
e
I
SC
X
-
U
R
L
2
0
1
6
d
ataset
o
u
g
h
t to
b
e
in
a
f
o
r
m
at
s
u
itab
le
f
o
r
M
L
al
g
o
r
ith
m
s
.
Dat
a
p
r
ep
r
o
ce
s
s
in
g
is
a
cr
u
cial
s
tep
in
v
o
lv
in
g
tr
an
s
f
o
r
m
in
g
r
a
w
d
ata
in
to
a
r
ef
in
ed
d
ataset
b
ef
o
r
e
in
p
u
tti
n
g
it
in
to
ML
al
g
o
r
ith
m
s
[
1
7
]
,
[
1
8
]
.
T
h
e
d
ata
f
o
r
m
at
m
u
s
t
b
e
ap
p
r
o
p
r
iate
to
ac
h
ie
v
e
o
p
ti
m
al
o
u
tco
m
e
s
f
r
o
m
th
e
M
L
alg
o
r
ith
m
s
u
til
ized
.
Fo
r
in
s
ta
n
ce
,
th
e
m
aj
o
r
ity
o
f
M
L
al
g
o
r
ith
m
s
ca
n
n
o
t
h
a
n
d
le
n
u
ll
v
al
u
es.
T
h
er
ef
o
r
e,
it
is
n
ec
es
s
ar
y
to
p
r
ep
r
o
ce
s
s
th
e
I
S
C
X
-
UR
L
2
0
1
6
d
ataset.
T
h
e
I
S
C
X
-
UR
L
2
0
1
6
d
ataset
co
n
tain
s
n
u
m
er
o
u
s
f
ea
tu
r
e
s
w
it
h
n
u
ll
v
al
u
es.
T
h
ese
f
ea
t
u
r
es
h
av
e
b
ee
n
e
x
cl
u
d
ed
f
r
o
m
th
e
d
ataset,
r
ed
u
cin
g
th
e
to
tal
n
u
m
b
er
o
f
f
ea
t
u
r
es
f
r
o
m
7
9
to
7
2
.
Seco
n
d
,
as
s
ee
n
i
n
T
ab
le
1
,
m
a
n
y
o
f
th
e
d
ataset
’
s
f
ea
t
u
r
es
i
n
cl
u
d
e
v
al
u
es
d
is
p
er
s
ed
ac
r
o
s
s
a
lar
g
e
r
an
g
e
o
f
v
al
u
es
[
1
7
]
,
[
1
8
]
.
T
h
ese
v
alu
es
h
a
v
e
b
ee
n
co
n
d
en
s
ed
in
to
r
elativ
el
y
t
ig
h
t
r
an
g
es
b
y
u
tili
z
in
g
th
e
Mi
n
-
Ma
x
s
ca
lin
g
n
o
r
m
aliz
in
g
ap
p
r
o
ac
h
[
1
1
]
,
[
1
9
]
.
T
a
b
le
2
p
r
esen
ts
s
a
m
p
le
s
o
f
t
h
e
I
SC
X
-
U
R
L
2
0
1
6
s
p
a
m
d
ataset
b
ef
o
r
e
an
d
af
ter
t
h
e
n
o
r
m
aliza
t
io
n
p
r
o
ce
s
s
.
L
a
s
t
b
u
t
n
o
t
least,
t
h
e
m
o
s
t
s
i
g
n
i
f
ica
n
t
f
ea
t
u
r
es
ar
e
s
elec
ted
(
th
o
s
e
p
r
o
v
id
in
g
th
e
h
ig
h
es
t
lev
el
o
f
p
er
f
o
r
m
an
c
e)
,
an
d
th
e
r
e
m
ain
in
g
f
ea
tu
r
es
ar
e
r
em
o
v
ed
,
as
d
etailed
in
th
e
f
o
llo
w
in
g
s
u
b
s
e
ctio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
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[
9
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1
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r
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1
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HHO
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[
9
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T
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tech
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I
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T
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A
T
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454
450
T
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Hy
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#
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3
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Na
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m
An
NB
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s
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al
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w
it
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clea
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s
u
m
p
tio
n
s
[
2
2
]
.
Sev
er
al
h
y
p
er
p
ar
a
m
eter
s
i
m
p
ac
t
th
e
NB
alg
o
r
ith
m
’
s
p
er
f
o
r
m
a
n
ce
.
T
h
ese
h
y
p
er
p
ar
a
m
eter
s
h
a
v
e
b
ee
n
t
u
n
ed
u
s
in
g
th
e
r
an
d
o
m
s
ea
r
c
h
alg
o
r
ith
m
.
T
ab
le
4
s
h
o
w
s
t
h
e
v
al
u
es
o
f
th
e
s
e
h
y
p
er
p
ar
a
m
eter
s
.
T
ab
le
4
.
Hy
p
er
p
ar
am
e
ter
s
o
f
t
h
e
NB
alg
o
r
ith
m
#
H
y
p
e
r
p
a
r
a
me
t
e
r
D
e
scri
p
t
i
o
n
V
a
l
u
e
1
a
l
p
h
a
A
d
d
i
t
i
v
e
smo
o
t
h
i
n
g
p
a
r
a
me
t
e
r
1
.
0
2
c
l
a
ss_
p
r
i
o
r
P
r
i
o
r
p
r
o
b
a
b
i
l
i
t
i
e
s o
f
t
h
e
c
l
a
sse
s
N
o
n
e
3
f
i
t
_
p
r
i
o
r
W
h
e
t
h
e
r
t
o
l
e
a
r
n
c
l
a
ss
p
r
i
o
r
p
r
o
b
a
b
i
l
i
t
i
e
s
T
r
u
e
2
.
3
.
3
.
Ada
B
o
o
s
t
cla
s
s
if
ica
t
io
n a
lg
o
rit
h
m
T
h
e
ad
a
p
tiv
e
A
d
aB
o
o
s
t
alg
o
r
ith
m
g
r
ad
u
all
y
tr
an
s
f
o
r
m
s
a
w
e
ak
class
i
f
ier
in
to
a
r
eliab
le
an
d
ef
f
ec
ti
v
e
o
n
e.
E
ac
h
cy
cle
u
s
es
th
e
w
ea
k
class
i
f
ier
to
class
if
y
th
e
v
al
u
es
in
t
h
e
tr
ain
in
g
d
ataset.
A
l
l
th
e
d
ata
v
alu
es
ar
e
ass
i
g
n
ed
eq
u
al
w
e
ig
h
ts
at
t
h
e
s
tar
t
o
f
th
e
tr
ai
n
i
n
g
p
r
o
ce
s
s
.
Nev
er
t
h
eles
s
,
w
ith
ea
c
h
s
u
b
s
eq
u
en
t
c
y
cle,
th
e
w
ei
g
h
t
o
f
t
h
e
i
n
co
r
r
ec
tl
y
ca
te
g
o
r
ized
d
ata
p
o
in
ts
in
cr
ea
s
e
s
,
m
ak
in
g
t
h
e
clas
s
i
f
ier
in
th
at
c
y
cle
r
el
y
m
o
r
e
o
n
th
e
m
.
As
a
r
esu
lt,
t
h
is
i
n
d
icat
es
a
d
r
o
p
in
th
e
class
if
ier
’
s
g
lo
b
al
er
r
o
r
,
cr
ea
tin
g
a
m
o
r
e
r
o
b
u
s
t
an
d
ef
f
ec
ti
v
e
class
i
f
ier
[
2
3
]
.
Sev
er
al
h
y
p
er
p
ar
a
m
eter
s
i
m
p
ac
t
t
h
e
A
d
aB
o
o
s
t
alg
o
r
ith
m
’
s
p
er
f
o
r
m
an
ce
.
T
h
ese
h
y
p
er
p
ar
a
m
eter
s
h
a
v
e
b
ee
n
t
u
n
ed
u
s
in
g
th
e
r
an
d
o
m
s
ea
r
c
h
alg
o
r
ith
m
.
T
ab
le
5
s
h
o
w
s
t
h
e
v
al
u
es
o
f
th
e
s
e
h
y
p
er
p
ar
a
m
eter
s
.
T
ab
le
5
.
Hy
p
er
p
ar
am
e
ter
s
o
f
t
h
e
A
d
aB
o
o
s
t
alg
o
r
ith
m
#
H
y
p
e
r
p
a
r
a
me
t
e
r
D
e
scri
p
t
i
o
n
V
a
l
u
e
1
n
_
e
st
i
ma
t
o
r
s
N
u
mb
e
r
o
f
w
e
a
k
l
e
a
r
n
e
r
s t
o
u
se
2
0
0
2
l
e
a
r
n
i
n
g
_
r
a
t
e
S
h
r
i
n
k
s
t
h
e
c
o
n
t
r
i
b
u
t
i
o
n
o
f
e
a
c
h
w
e
a
k
l
e
a
r
n
e
r
0
.
1
3
a
l
g
o
r
i
t
h
m
A
l
g
o
r
i
t
h
m
t
o
u
se
‘
S
A
M
M
E.
R
’
4
b
a
se
_
e
st
i
ma
t
o
r
T
h
e
b
a
se
e
st
i
m
a
t
o
r
f
r
o
m w
h
i
c
h
t
h
e
b
o
o
st
e
d
e
n
se
mb
l
e
i
s
b
u
i
l
t
D
T
(
max
_
d
e
p
t
h
=
3
)
5
r
a
n
d
o
m_
s
t
a
t
e
C
o
n
t
r
o
l
s t
h
e
r
a
n
d
o
m
n
e
ss o
f
t
h
e
e
st
i
m
a
t
o
r
1
2
3
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
ese
n
ts
a
n
d
d
is
cu
s
s
es
t
h
e
r
es
u
lts
.
T
h
r
ee
M
L
alg
o
r
ith
m
s
w
it
h
t
u
n
ed
p
ar
am
eter
s
(
s
e
e
s
u
b
s
ec
tio
n
2
.
3
)
ar
e
u
s
ed
:
DT
,
A
d
aB
o
o
s
t,
an
d
NB
.
T
h
e
r
es
u
lts
w
er
e
atta
in
ed
u
s
i
n
g
a
P
C
w
it
h
t
h
e
f
o
llo
w
i
n
g
s
o
f
t
w
ar
e
an
d
h
ar
d
w
ar
e
s
p
ec
if
icatio
n
s
:
A
ce
r
Asp
ir
e
E
5
-
5
7
5
G
m
o
d
el,
W
in
d
o
w
s
1
0
P
r
o
,
6
4
-
b
it
O.
S
s
y
s
te
m
t
y
p
e,
I
n
tel
C
o
r
e
i5
-
7
2
0
0
C
P
U
(
2
.
5
0
GHz
s
p
ee
d
,
2
C
o
r
e
s
,
an
d
4
T
h
r
ea
d
s
)
,
1
6
GB
R
A
M,
a
n
d
P
y
th
o
n
p
r
o
g
r
am
m
i
n
g
lan
g
u
ag
e.
T
h
e
p
er
f
o
r
m
an
ce
m
etr
ics
ar
e
b
ased
o
n
th
e
co
n
f
u
s
io
n
m
atr
i
x
(
Fig
u
r
e
2
)
.
A
co
n
f
u
s
io
n
m
atr
i
x
s
u
m
m
ar
izes
th
e
n
u
m
b
er
o
f
r
ig
h
t
an
d
w
r
o
n
g
p
r
ed
ictio
n
s
m
ad
e
in
a
class
if
icatio
n
is
s
u
e,
b
r
o
k
en
d
o
w
n
b
y
class
an
d
s
u
m
m
ar
ized
w
it
h
co
u
n
t
v
al
u
es.
T
h
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
u
s
ed
a
r
e
ac
cu
r
ac
y
,
r
ec
all,
p
r
ec
is
io
n
,
an
d
F1
-
s
co
r
e.
A
cc
u
r
ac
y
(
1
)
is
th
e
d
ataset
’
s
n
u
m
b
er
o
f
co
r
r
ec
t
s
p
am
a
n
d
h
a
m
cla
s
s
i
f
icat
io
n
s
.
P
r
ec
is
io
n
(
2
)
is
th
e
r
atio
o
f
tr
u
e
p
o
s
iti
v
e
to
t
h
e
s
u
m
o
f
f
al
s
e
p
o
s
iti
v
e
a
n
d
tr
u
e
p
o
s
iti
v
e.
R
ec
all
(
3
)
is
t
h
e
r
atio
o
f
tr
u
e
p
o
s
iti
v
e
to
t
h
e
s
u
m
o
f
f
alse
n
e
g
ati
v
e
an
d
tr
u
e
p
o
s
itiv
e.
F1
-
s
co
r
e
(
4
)
is
a
m
e
asu
r
e
o
f
class
i
f
icatio
n
ac
cu
r
ac
y
o
n
a
d
ataset
th
at
p
r
o
v
id
es a
b
alan
ce
b
et
w
ee
n
p
r
ec
is
io
n
a
n
d
r
ec
all
[
6
]
,
[
24
]
,
[
2
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
E
n
h
a
n
ci
n
g
s
p
a
m
d
etec
tio
n
u
s
i
n
g
Ha
r
r
is
Ha
w
ks o
p
timiz
a
tio
n
a
lg
o
r
ith
m
(
Mo
s
leh
M.
A
b
u
a
lh
a
j
)
451
Fig
u
r
e
2
.
C
o
n
f
u
s
io
n
m
atr
ix
=
(
+
)
(
+
+
+
)
(
1
)
=
(
+
)
(
2
)
=
(
+
)
(
3
)
1
−
=
2
×
×
+
(
4
)
Fig
u
r
es
3
to
6
s
h
o
w
th
e
ac
cu
r
ac
y
,
r
ec
all,
p
r
ec
is
io
n
,
an
d
F1
-
s
co
r
e,
r
esp
ec
tiv
el
y
.
T
h
e
r
esu
lts
ac
h
iev
e
d
b
y
th
e
DT
alg
o
r
ith
m
f
o
r
all
m
etr
ics
ar
e
9
9
.
7
5
%.
T
h
e
r
esu
lts
ac
h
iev
ed
b
y
th
e
NB
alg
o
r
i
th
m
ar
e
as
f
o
llo
w
s
:
ac
cu
r
ac
y
i
s
9
6
.
3
0
%%
,
r
ec
all
is
9
6
.
3
0
%%
,
p
r
ec
is
io
n
i
s
9
6
.
1
9
%,
an
d
F1
-
s
co
r
e
is
9
6
.
1
0
%,
r
esp
ec
tiv
el
y
.
T
h
e
r
esu
lt
s
ac
h
iev
ed
b
y
t
h
e
A
d
aB
o
o
s
t
alg
o
r
ith
m
f
o
r
all
m
etr
ic
s
ar
e
9
9
.
6
7
%.
T
h
e
DT
ac
h
iev
ed
th
e
h
i
g
h
e
s
t
r
es
u
lt
s
o
f
th
e
th
r
ee
alg
o
r
ith
m
s
w
it
h
all
m
etr
ics,
o
u
tp
er
f
o
r
m
i
n
g
th
e
NB
b
y
3
.
4
5
%
w
i
th
ac
cu
r
ac
y
a
n
d
r
ec
all
,
3
.
6
5
%
w
it
h
F1
-
s
co
r
e,
an
d
3
.
5
6
%
w
it
h
pr
ec
is
io
n
,
a
n
d
o
u
tp
er
f
o
r
m
ed
A
d
aB
o
o
s
t b
y
0
.
0
8
%
w
ith
al
l m
etr
ics.
Fig
u
r
e
3
.
A
cc
u
r
ac
y
o
f
t
h
e
HH
O
alg
o
r
ith
m
Fig
u
r
e
4
.
R
ec
all
o
f
t
h
e
HHO
a
lg
o
r
ith
m
9
9
.7
5
%
9
6
.3
0
%
9
9
.6
7
%
DT
NB
A
D
A
B
O
O
S
T
A
C
C
U
R
A
C
Y
(
%
)
M
E
T
H
O
D
A
C
C
U
R
A
C
Y
9
9
.7
5
%
9
6
.3
0
%
9
9
.6
7
%
DT
NB
A
D
A
B
O
O
S
T
R
E
C
A
L
L
(
%
)
M
E
T
H
O
D
R
E
C
A
L
L
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
2
3
,
No
.
2
,
A
p
r
il
20
2
5
:
4
4
7
-
454
452
Fig
u
r
e
5
.
P
r
ec
is
io
n
o
f
th
e
HH
O
alg
o
r
ith
m
Fig
u
r
e
6
.
F1
-
s
co
r
e
o
f
th
e
HH
O
alg
o
r
ith
m
4.
CO
NCLU
SI
O
N
Sp
a
m
e
m
ail
s
ar
e
o
n
e
o
f
th
e
th
r
ea
ts
th
a
t
co
m
p
a
n
ies
m
u
s
t
f
ac
e.
A
ttac
k
er
s
ex
p
lo
it
s
p
a
m
em
ai
ls
to
s
p
r
ea
d
v
ar
io
u
s
t
y
p
es
o
f
at
tack
s
.
I
n
th
i
s
p
ap
er
,
th
e
ML
alg
o
r
ith
m
s
ar
e
e
m
p
lo
y
ed
to
m
i
tig
a
t
e
th
e
s
p
r
ea
d
o
f
th
e
s
p
a
m
e
m
ail
s
.
First,
t
h
e
HH
O
alg
o
r
ith
m
i
s
u
tili
ze
d
to
id
en
t
i
f
y
th
e
k
e
y
f
ea
tu
r
es
th
a
t
h
elp
t
o
d
is
tin
g
u
i
s
h
s
p
a
m
f
r
o
m
h
a
m
e
m
ails
.
T
h
e
HHO
alg
o
r
ith
m
h
as
r
ed
u
ce
d
t
h
e
7
2
f
ea
t
u
r
es
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r@u
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.
m
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.