Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization

Abstract

Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications.

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

“…combined a mutation operator as a local search procedure with CSO algorithm to find better solutions in the area of the global best [26]. It is then used to optimize the feature selection and parameters of the support vector machine.…”

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

“…search procedure with a CSO algorithm to find better solutions in the area of the global best [29]. It is then used to optimize the feature selection and parameters of the support vector machine.…”

Otimização do desempenho de um classificador por modificação no processo de seleção de características

“…Otimização dos seus parâmetros de configuração podem ser feitos para melhor generalização. Testes comparativos com outros algoritmos baseados em computação evolucionária, como o MSCO [12], podem ajudar em sua melhor validação.…”

“…Diversos trabalhos lidam com a otimização do processo de seleção de características por meio de algoritmos genéticos [9,10,11]. Recentemente, [12] propôs o algoritmo MCSO. Este procura otimizar a seleção de características junto ao SVM, utilizando técnicas de computação evolucionária.…”