Discretization Based Support Vector Machines for Classification

Abstract

Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers, which are more concise to represent and specify, easier to use and comprehend, as they are closer to a knowledge-level representation than continuous ones. Discretization is the process of quantizing continuous attributes. It has been used for decision tree classifier. The success of discretization can significantly extend the borders of many learning algorithms. Support vector Machines are the new generation learning system based on the latest advances in statistical learning theory. SVM is the recent addition to the toolbox of the data mining practitioners and are gaining popularity due to many attractive features, and discretization is looked into. The classification results achieved after discretization has been instrumental this is an attempt to extend the boundaries of discretization and to evaluate on other machine learning techniques for classification namely, support vector machines.