A Wrapper-Based Combined Recursive Orthogonal Array and Support Vector Machine for Classification and Feature Selection

Wei-Chang Yeh, Yuan-Ming Yeh, Cheng-Wei Chiu, Yuk Ying Chung

Abstract


In data mining, classification problems are among the most frequently discussed issues. Feature selection is a very important pre-processing function in the vast majority of classification cases. Its aim is to delete irrelevant or redundant features in order to reduce the feature dimension and computing complexity and increase the accuracy of classification. Current feature selection methods can be roughly divided into the filter method and the wrapper method. The former chooses the feature subset before classifying, whereas the latter chooses the feature subset during the classification procedure. In general, wrapper methods result in better performance than filter methods, but they are time-consuming. This paper therefore proposes a wrapper method called OA-SVM that uses an orthogonal array (OA) to make systemic rules of feature selection and uses support vector machine (SVM) as the classifier. The proposed OA-SVM is employed to test eight UCI databases for the classification problem. The results of these experiments verify that the proposed OA-SVM for feature selection can effectively delete irrelevant or redundant features, thereby increasing classification accuracy.


Full Text: PDF DOI: 10.5539/mas.v8n1p11

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This work is licensed under a Creative Commons Attribution 3.0 License.

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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