Information Technology Reference
In-Depth Information
n
∑
=
b
=
arg
max
d
(2)
i
j
i
j
1
Another method to combine a number of classifiers which employs
d
ij
as
confidence of classifier
j
for belonging the test data sample to class
i
is called majority
average voting. The majority average voting uses equation 2 as majority voting.
Weighted majority vote is another approach of voting; in this method members'
votes have different worths. Unlike the previous versions of voting this is not like
democracy. For example if a classifier has 99% recognition ratio, it is more worthy to
use its vote with a more effect than the vote of another classifier with 80% accuracy
rate. Therefore in weighted majority vote approach, every vote is multiplied by its
worth. Kuncheva [7] has shown that this worth can optimally be a function of
accuracy.
To sum up assume that the classifiers existing in the ensemble
E
have accuracies
{
p
1
, p
2
, p
3
…p
n
} respectively. According to Kuncheva [7] the worth of them are
{
w
1
, w
2
, w
3
…w
n
} respectively where
p
i
w
=
log
(3)
i
1
−
p
i
Weighted majority vote mechanism decides the data sample to belong class
b
according to equation 2.
n
∑
=
b
=
arg
max
w
*
d
(4)
j
i
j
i
j
1
Similarly another method of combining which again employs
d
ij
as confidence of
classifier
j
for belonging the test data sample to class
i
is called weighted majority
average voting. Weighted majority average voting uses equation 4 as weighted
majority voting.
3 Feature Extraction
Gabor filter can capture salient visual properties such as the spatial localization, the
orientation selectivity, and the spatial frequency characteristics. The Gabor responses
describe a small patch of gray values in an image
I
(
x
) around a given pixel
x
=(
x,y
)
T
. It
is based on a wavelet transformation, given by the equation 5.
∫
R
(
x
)
=
I
(
x
'
)
ψ
(
x
−
x
'
)
dx
'
(5)
i
i
which
ψ
(
x
)
is a convolution of image with a family of Gabor kernels like equation 6.
i
2
2
2
k
x
⎡
2
⎤
σ
k
i
−
2
i
2
jk
x
ψ
(
x
)
=
e
2
σ
e
−
e
(6)
⎢
⎣
⎥
⎦
i
i
2
σ
⎢
⎥
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