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Title

An iterative algorithm based on Fuzzy Support Vector Machine Classifier

Author

Saeed Khodayi, Mehdi Fatan

Citation

Vol. 17  No. 7  pp. 320-323

Abstract

Regarding progresses in data collection and storage capabilities in recent decades, high- dimensional dataset is quickly increasing in different disciplines. Most of these datasets have many features and relatively less patterns. Most of these features are mostly unrelated and redundant which leads to reduction of classification algorithms performance. Therefore, feature selection is proposed for reducing the dimensions of problem and increasing the efficiency of classification algorithms. In this paper, a new method is presented for improving the function of data classification. In proposed method, Fuzzy Support Vector Machine classifier algorithms is used for removing unrelated features in data. The performance of proposed method is compared with the newest and most known methods based on support vector machine classifier. The results of tests showed that the proposed method has good performance due to classification accuracy.

Keywords

data analysis, feature selection, classification, support vector machine

URL

http://paper.ijcsns.org/07_book/201707/20170746.pdf