Feature Selection using PSO-SVM

Feature Selection using PSO-SVM,Chung-jui Tu,Li-yeh Chuang,Jun-yang Chang,Cheng-hong Yang

Feature Selection using PSO-SVM   (Citations: 15)
BibTex | RIS | RefWorks Download
The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Feature selection is of great importance in pattern classification, medical data processing, machine learning, and data mining applications. Therefore, a good feature selection method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and support vector machines (SVMs) with the one-versus-rest method serve as a fitness function of PSO for the classification problem. The proposed method is applied to five classification problems from the literature. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to the other feature selection methods.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: