Feature Selection Using Different Transfer Functions for Binary Bat Algorithm

International Journal of Mathematical, Engineering and Management Sciences

Omar Saber Qasim¹  Zakariya Y. Algamal² 

[1] Department of Mathematics, University of Mosul, Mosul, Iraq;Department of Statistics and Informatics, University of Mosul, Mosul, Iraq;

关键词: feature subset selection;    bat algorithm;    transfer function;    metaheuristic algorithms;   

DOI  :  10.33889/IJMEMS.2020.5.4.056

来源: DOAJ

【 摘 要 】

The selection feature is an important and fundamental step in the preprocessing of many classification and machine learning problems. The feature selection (FS) method is used to reduce the amount of data used and to create high-probability of classification accuracy (CA) based on fewer features by deleting irrelevant data that often reason confusion for the classifiers. In this work, bat algorithm (BA), which is a new metaheuristic rule, is applied as a wrapper type of FS technique. Six different types of BA (BA-S and BA-V) are proposed, where apiece used a transfer function (TF) to map the solutions from continuous space to the discrete space. The results of the experiment show that the features that use the BA-V methods (that is, the V-shaped transfer function) have proven effective and efficient in selecting subsets of features with high classification accuracy.

【 授权许可】

Unknown