複合属性による領域分割を用いた決定木DTMACC

抄録

A decision tree is one of the machine learning techniques and also one of the major knowledge representations of data mining results.This is because it is easy to understand its meaning for human analysts.Even ID3, the representative algorithm, is known to exhibit remarkable performance deterioration under certain circumstances, particularly due to strong correlation between attributes representing the class of examples. One of the approaches to get more preferable decision trees is pre-processing the training data to extend its description, such as attributes generation and attribute selection. There is also the idea of decision trees with a region rule. In this paper, we consider two approaches, i.e., decision trees with a region rule allowing multiple attributes, and a pre-processing method of a region rule to enabling any suitable number of attributes to correspond to branch nodes, where an optimal division condition with arbitrarily multiple attributes is acquired. By using this method, we propose a new decision tree generation algorithm guaranteeing to select effective compound attributes with each branch node, where an MDL-based new evaluation criterion is also defined for determining the optimal number of compound attributes specified to each node.This algorithm is applied to datasets containing only nominal values. It consists of three processes: compound attributes selection, parent node integration, and pruning. We call this new decision trees DTMACC (Decision Trees with Multiple Attributes Concept Clustering). The effectiveness and comprehensiveness of the proposed algorithm are confirmed through experiments comparing to the ordinary decision trees and an effective pre-processing method.