Evaluation of Feature Selection Method for Classification of Data Using Support Vector Machine Algorithm

One may claim that the exponential growth in the amount of data provides great opportunities for data mining. In many real world applications, the number of sources over which this information is fragmented grows at an even faster rate, resulting in barriers to widespread application of data mining. This paper proposes feature selection by using Support Vector Machine is to reduce the computational complexity and increase the classification accuracy of the selected feature subsets and also paper evaluates the approach by comparing it with existing feature selection algorithms over 6 datasets from University of California, Irvine (UCI) machine learning databases. The proposed method shows better results in terms of number of selected features, classification accuracy, and running time than most existing algorithms.