一种加权聚类划分决策树算法

针对斜划分决策树算法普遍存在时间效率低、部分算法仅能应用于二分类问题，提出了一种基于加权距离的聚类决策树算法。通过Relief-F算法为预测属性计算权重，并将权重用于树结点中数据的聚类过程，使用分簇结果对结点进行多路划分，得到可直接用于多分类问题的决策树。理论分析和实验结果表明，该算法与经典轴平行决策树相比，拥有更好的泛化能力以及相近的算法时间复杂度，与大部分斜决策树相比，在付出更少计算代价的前提下，获得了近似的正确率以及模型简洁度。

A Weighted Clustering Splitting Decision Tree Algorithm

To solve the problems that the time efficiency of the oblique decision tree is low and a few algorithms are used only in binary classification,a clustering decision tree algorithm based on weighted distance(WCDT) is proposed.Weights are calculated for prediction attributes by Relief-F,and applied during the clustering of tree nodes.The clustering results are serviced in multi-way splits in order to apply in the decision tree for multi-classification problems.Theoretical analysis and experimental results show that compared with the classical axis parallel decision trees,the proposed algorithm has better generalization ability and similar algorithm time complexity;and compared with the most oblique decision trees,it has obtained an approximate accuracy and simplicity in case of less computational.