Classification Technique – Construction of Decision Tree with Continuous Variable

Abstract

Decision tree supervised learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed, such as ID3, Assistant and C4.5. These algorithms suffer from some drawbacks. In traditional classification tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of non overlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive analysis shows that the proposed method outperforms the preprocessing approach, the regression tree approach, which presents the best approach comparing to existing algorithms

 

 

 

Keywords – Data mining, Classification, Decision tree, ID3, CART.