Abstract
Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.
Highlights
Support vector machine (SVM) was first proposed by Vapnik [1] and has recently been applied in a range of problems including pattern recognition, bioinformatics and text categorization
We combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed genetic algorithm (GA)-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and particle swarm optimization (PSO)-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models
The work is novel, since few researches have conducted on the GA-FSSVM and PSOFSSVM classification system to find simultaneously an optimal feature subset and SVM model parameters in high-dimensional data classification
Summary
Support vector machine (SVM) was first proposed by Vapnik [1] and has recently been applied in a range of problems including pattern recognition, bioinformatics and text categorization. SVM classifies data with different class labels by determining a set of support vectors that are members of the set of training inputs that outline a hyperplane in the feature space. When using SVM, two issues should be solved: how to choose the optimal input feature subset for SVM, and how to set the best kernel parameters. The two issues are solved separately ignoring their close connections, this always leads low classification accuracy. These two problems are crucial, because the feature subset choice influences the appropriate kernel parameters and vice versa [2]. Obtaining the optimal feature subset and SVM parameters must occur simultaneously
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