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Feature Selection Using GSA-BBO Hybridization Algorithm with Chaos by Supailin Pichai; Khamron Sunat is a scholarly article available to read on EtoBox.

What is Feature Selection Using GSA-BBO Hybridization Algorithm with Chaos about?

Feature selection (FS) is a crucial data preprocessing process in classification problems. Feature selection is one of major challenges and aimed to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. Most existing feature selection algorithms treat the task as a single objective problem. In this paper, a novel metaheuristic optimizer, namely chaotic GSA-BBO hybridization algorithm (CGSA-BBO), is proposed to alleviate optimize feature selection the problem. In order to improve the feature selection techniques, this paper applies CGSA-BBO to optimize the feature selection process on 14 standard benchmark datasets, obtained from UCI repository. Four chaotic maps are employed during the optimization process. The experimental results demonstrate the effectiveness of chaotic GSA-BBO with Intermittency map. The proposed technique is superior to the basic GSABBO and other investigated algorithms. In addition, CGSA-BBO is able to obtain an optimal feature selection, which maximizes the classif

Author

Supailin Pichai; Khamron Sunat

Publisher

IEEE

Published

2019

Language

EN