A new decision tree induction using heuristic

Abstract

One effective method for gleaning categorization information from a collection of feature-based cases is decision tree induction. The minimal entropy is the most common heuristic data utilised in the development of decision trees. The limited ability to generalise is a key drawback of this heuristic information . Based on statistical learning theory, support vector machine (SVM) is a machine learning classification approach. It is broadly applicable. The big margin of the support vector machine (SVM) can be employed as the heuristic information of the decision tree, in order to increase its generalisation capability, taking into account the relationship between the classification margin of SVM and the generalisation capability.