Multi-variable decision tree construction and research
YU Zhen-zhou · Computer Engineering and Applications Journal · 2010
Decision tree algorithm in univariate tests causes large-scale,complex rules that are difficult to understand.Multi-variable decision tree is effectively used in the classification of data mining.The key to build it lies in the reasonable choice of attributes combination based on the interconnection between attributes.Based on the rough set theory of attribute dependability and the concept of conditional attributes dispersion degree in information system,a new multi-variable decision tree algorithm called RD is proposed.The results of experiments on the UCI show that the decision tree built by the proposed method has better classification results than those of ID3 algorithm and multi-variate decision tree construction algorithm based on the relative core of attributes.