Open AccessA novel SVM parameter tuning method based on advanced whale optimization algorithm
Author(s) -
Xuehao Yin,
Yandong Hou,
Jiabao Yin,
Chao Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/2/022140
Subject(s) - support vector machine , convergence (economics) , whale , optimization algorithm , computer science , algorithm , mathematical optimization , artificial intelligence , mathematics , fishery , economics , biology , economic growth
The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).