一种多变量决策树方法研究

A New Multivariate Decision Tree Algorithm

单变量的决策树算法造成树的规模庞大,规则复杂,不易理解。本文结合粗糙集原理中的相对核及加权粗糙度的方法,提出了一种新的多变量决策树算法。通过实例表明,本文的多变量决策树方法产生的决策树比传统的ID3算法构造的决策树更简单,具有较好的分类效果。

Decision Tree Algorithm in univariate tests caused large-scale, complex rules that are difficult to understand. Based on the rough sets theory of attributes reduction, the core of condition attributions and the Weighted roughness Of condition attributions, a new multivariate decision tree algorithm is proposed. A example shows in this paper, the decision tree built by the method is more simple and has better classification result than that of ID3 algorithm.