Bat algorithm and k-means techniques for classification performance improvement

Rozlini Mohamed (Universiti Tun Hussein Onn Malaysia)
Munirah Mohd Yusof (Universiti Tun Hussein Onn Malaysia)
Noorhaniza Wahid (Universiti Tun Hussein Onn Malaysia)
Norhanifah Murli (Universiti Tun Hussein Onn Malaysia)
Muhaini Othman (Universiti Tun Hussein Onn Malaysia)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 15, No 3: September 2019

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.