Discretization based Support Vector Machine (D-SVM) for Classification of Agricultural Datasets

Abstract

Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are concise to represent and specify, easier to use and comprehend as they are closer to the knowledge level representation than continuous ones. Data is reduced and simplified using discretization and it makes the learning more accurate and faster [3]. Support Vector Machine (SVM) developed by [15] is a novel learning method based on statistical learning theory. SVM is a powerful tool for solving classification problems with small samples, nonlinearities and local minima, and has been of excellent performance. In this paper, a new approach to classify data using discretization based SVM classifier, is discussed. This is an attempt to extend the boundaries of discretization and to evaluate its effect on other machine learning techniques for classification namely, support vector machines.