Binary JAYA Algorithm with Adaptive Mutation for Feature Selection
- PDF / 1,021,161 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 64 Downloads / 255 Views
RESEARCH ARTICLE-COMPUTER ENGINEERING AND COMPUTER SCIENCE
Binary JAYA Algorithm with Adaptive Mutation for Feature Selection Mohammed A. Awadallah1 · Mohammed Azmi Al-Betar2,3 · Abdelaziz I. Hammouri4 · Osama Ahmad Alomari5 Received: 17 February 2020 / Accepted: 13 August 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate (Rm ) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results. Keywords JAYA algorithm · Feature selection · Machine learning · Metaheuristic · Optimization
B
List of Symbols Mohammed A. Awadallah [email protected] Mohammed Azmi Al-Betar [email protected] Abdelaziz I. Hammouri [email protected] Osama Ahmad Alomari [email protected]
1
Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
2
Department of Information Technology - MSAI, College of Engineering and Information Technology, Ajman University, Ajman, UAE
3
Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan
4
Department of Computer Information Systems, Al-Balqa Applied University, 19117 Al-Salt, Jordan
5
Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, Turkey
Abbreviations AV Average of the results BBA Binary bat algorithm BGOA Binary grasshopper optimization algorithm BGSA Binary gravitational search algorithm BGWO
Data Loading...
