Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method

Abstract

Data mining techniques, extracting patterns from large databases have become widespread in all life’s aspect. One of the most important data mining tasks is classification. Classification is an important and widely studied topic in many disciplines, including statistics, artificial intelligent, operations research, computer science and data mining and knowledge discovery. One of the important things that should be done before using classification algorithms is preprocessing operations which cause to improve the accuracy of classification algorithms. Preprocessing operations include various methods that one of them is normalization. In this paper, we selected five applicable normalization methods and then we normalized selected data sets afterward we calculated the accuracy of classification algorithm before and after normalization. In this study the SVM algorithm was used in classification because this algorithm works based on n-dimension space and if the data sets become normalized the improvement of results will be expected. Eventually Data Envelopment Analysis (DEA) is used for ranking normalization methods. We have used four data sets in order to rank the normalization methods due to increase the accuracy then using DEA and AP-model outrank these methods.