Data Classification: A Rough - SVM Approach

Abstract

Classification is one of the most important tasks for different applications. Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. SVM, a powerful machine method developed from statistical learning and has made significant achievement in some field. Introduced in the early 90’s, they led to an explosion of interest in machine learning. The foundations of SVM have been developed by Vapnik and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. SVM method does not suffer the limitations of data dimensionality and limited samples. This paper reports the introduction of Rough -SVM Approach based on the hybridization of SVM and Rough Set Exploration System (RSES). RSES is used to find reducts which then applied to SVM to obtain better classification results.