A Novel Decision Tree Framework using Discrete Haar Wavelet Transform

Abstract

Data Mining is a popular knowledge discovery technique. In data mining decision trees are of the simple and powerful decision making models. One of the limitations in decision trees is towards the data source which they tackle. If data sources which are given as input to decision tree are of imbalance nature then the efficiency of decision tree drops drastically, we propose a decision tree structure which uses discrete haar wavelet transformation technique along with a filter. In this paper, we propose a novel method WT Tree based on above strategy. Extensive experiments, using C4.5 decision tree as base classifier, show that the performance measures of our method is comparable to state-of-the-art methods.