MATEC Web of Conferences
Volume 150, 2018Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
|Number of page(s)||6|
|Section||Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM)|
|Published online||23 February 2018|
A Comparative Study of Feature Selection Techniques for Bat Algorithm in Various Applications
Department of Software Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Batu Pahat, Johor, Malaysia
2 Department of Multimedia, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Batu Pahat, Johor, Malaysia
* Corresponding author: firstname.lastname@example.org
Feature selection is a process to select the best feature among huge number of features in dataset, However, the problem in feature selection is to select a subset that give the better performs under some classifier. In producing better classification result, feature selection been applied in many of the classification works as part of preprocessing step; where only a subset of feature been used rather than the whole features from a particular dataset. This procedure not only can reduce the irrelevant features but in some cases able to increase classification performance due to finite sample size. In this study, Chi-Square (CH), Information Gain (IG) and Bat Algorithm (BA) are used to obtain the subset features on fourteen well-known dataset from various applications. To measure the performance of these selected features three benchmark classifier are used; k-Nearest Neighbor (kNN), Naïve Bayes (NB) and Decision Tree (DT). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure and ROC. The objective of these study is to analyse the outperform feature selection techniques among conventional and heuristic techniques in various applications.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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