Feature Selection Using Different Transfer Functions for Binary Bat Algorithm

Omar Saber Qasim,Zakariya Y Algamal

doi:10.33889/ijmems.2020.5.4.056

Omar Saber Qasim, Zakariya Y Algamal

Open Access

https://doi.org/10.33889/ijmems.2020.5.4.056

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Abstract

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.

Highlights

Feature selection plays an important role in the classification and machine learning, as it aims to remove the irrelevant or redundant features of the data to achieve better classification performance
The filter relies on the evaluates the subgroups based on the original dataset itself whereas the wrapper method is based on external learning algorithms based on metaheuristic algorithms to evaluate subgroups (Wang and Alexander, 2016; Teng et al, 2017; Al-Tashi et al, 2020)
Transfer functions (TFs) are important and effective parts of a binary metaheuristics algorithm when used in the classification process and feature selection (Mirjalili and Lewis, 2013), where it very affects the process of balance

Summary

Introduction

Feature selection plays an important role in the classification and machine learning, as it aims to remove the irrelevant or redundant features of the data to achieve better classification performance. Several metaheuristic algorithms have been proposed in conjunction with feature selection methods to reduce data processing time and improve the classification accuracy (CA) (Qasim and Algamal, 2018; Dahiya et al, 2019).

Results

Conclusion