A ReliefF guided binary equalization optimizer based on Z-type and reverse Z-type transfer functions for feature selection

Abstract

Feature selection (FS) refers to the process of finding the most relevant feature subset according to some criteria. A ReliefF guided novel binary equilibrium optimizer (RG-NBEO) feature selection method was put forward to solve the FS problem. Z-type transfer functions and reverse Z-type transfer functions are proposed to convert the continuous searching space into binary searching space. The ReliefF guidance strategy was adopted to achieve the increasing and decreasing of features in the iterative process. The proposed feature selection method was tested by using 10 UCI standard datasets. Firstly, the Z-type and Rz-type transfer functions with different parameters are compared, and the transfer function with best performance is selected to compare with S-, V- and U-type transfer functions. Then the proposed transfer function is applied to five wrapped feature selection algorithms, and the simulation results are analyzed statistically. Finally, the proposed transfer function is applied to other machine learning algorithms to verify the applicability of the proposed function. Simulation results verify the effectiveness of the proposed Z-type and Rz-type transfer function and ReliefF guided binary equalization optimizer.