Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

Algorithm 1

Cost-sensitive ReliefF feature select algorithm.

	Input: D: data in sliding window; F: feature set; k: number of neighbors; r: number of iterations
	Output: Feature weight vector
(1)	Begin
(2)	for all f ∈ F do
(3)	Wf = 0;
(4)	end for
(5)	for i = 1 to r do
(6)	random select x ∈ D;
(7)	sampling xj ∈ D, if yj = y then add xj to Hi, otherwise add to Mj, until |Hi| = |Mj| = k;
(8)	end for
(9)	for all f ∈ F do
(10)	Update Wf according to equation (5);
(11)	endfor
(12)	endfor
(13)	Select the top p% of the features;
(14)	end.