A BA-based algorithm for parameter optimization of Support Vector Machine

Citation data:

Pattern Recognition Letters, ISSN: 0167-8655, Vol: 93, Page: 13-22

Publication Year:
2017
Usage 11
Abstract Views 9
Link-outs 2
Captures 17
Readers 17
Social Media 136
Shares, Likes & Comments 136
Citations 19
Citation Indexes 19
DOI:
10.1016/j.patrec.2016.10.007
Author(s):
Alaa Tharwat; Aboul Ella Hassanien; Basem E. Elnaghi
Publisher(s):
Elsevier BV
Tags:
Computer Science
article description
Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter ( C ) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.