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Let m i represents the membership value of a positive data point x i , while
m i represents the membership of a negative data point x i in their own classes.
In the proposed FSVM-CIL method, these membership functions are defined as
follows:
m i
= f(x i )r +
(5.17)
m i
= f(x i )r
(5.18)
where f(x i ) generates a value between 0 and 1, which reflects the importance
of x i in its own class. The values for r + and r were assigned in order to reflect
the class imbalance, such that r + =
1and r =
r , where r is the minority-to-
majority-class ratio (r + >r ) (this was following the findings reported in [10],
where optimal results could be obtained from the DEC method when C /C +
equals to the minority-to-majority-class ratio). According to this assignment of
values, a positive class data point is assigned a misclassification cost m i C , where
m i takes a value in the [0,1] interval, while a negative class data point is assigned
a misclassification cost m i C , where m i
takes value in the [0 ,r ] interval, where
r< 1.
In order to define the function f(x i ) introduced in Equations 5.17 and 5.18,
which gives the within-class importance of a training example, the following
methods have been considered in [40].
A. f(x i ) is based on the distance from the own class center: In this method,
f(x i ) is defined with respect to d ce i , which is the distance between x i and its
own class center. The examples closer to the class center are treated as more
informative and assigned higher f(x i ) values, while the examples far away from
the center are treated as outliers or noise and assigned lower f(x i ) values. Here,
two separate decaying functions of d cen
i
have been used to define f(x i ) ,which
are represented by f cen
lin (x i ) and f cen
exp (x i ) as follows:
f cen
(d cen
i
/( max (d cen
i
lin (x i )
=
1
)
+
δ))
(5.19)
is a linearly decaying function. δ is a small positive value used to avoid the case
where f(x i ) becomes zero.
f cen
exp (x i ) = 2 /( 1 + exp (d cen
β))
(5.20)
i
is an exponentially decaying fu nc ti o n, where β ; β
[0 , 1] determines the steep-
2
ness of the d e cay. d cen
i
= x i x
is the Euclidean distance to x i from its own
class center x .
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