An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

Algorithm 2

NBPSOSEE-SBS feature selection.

(1)	Input: the data set after feature extracted
(2)	Output: best feature subset
(3)	Get training set and test set after the features have been extracted;
(4)	Initialize population, such as initial position, velocity and fitness values of particles, as well as local optimal pbest and global optimal of particles;
(5)	for k = 1 ⟶ maxIterations do
(6)	for i = 1 ⟶ swarmSize do
(7)	Update inertia weight using equation (10);
(8)	for j = 1 ⟶ dimension do
(9)	Update and ;
(10)	Update and , respectively, using equations (19) and (20);
(11)	Update and , respectively, using equations (13) and (14);
(12)	Update velocity of particle using equation (11);
(13)	Update position of particle using equation (18);
(14)	end for
(15)	end for
(16)	for i = 1 ⟶ swarmSize do
(17)	Update fitness value ;
(18)	Update local optimal pbest;
(19)	end for
(20)	Update global optimal ;
(21)	end for
(22)	Getting the optimal feature subset selected by NBPSOSEE;
(23)	Delete a feature in the current feature subset using equation (26);
(24)	Update the optimal feature subset using equation (27);
(25)	Repeat steps 23 and 24 until the termination condition is met;