| 摘要: |
| 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。 |
| 关键词: 机器学习 决策树 聚类 属性加权 多路划分 |
| DOI： |
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| 基金项目:国家自然科学基金青年基金资助项目(61602075) |
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| A Weighted Clustering Splitting Decision Tree Algorithm |
| LIU Zhenyu,CHU Na |
| (1.School of Computer and Science and Engineering,Northeastern University,Shenyang 110819,China;
2.College of Computer and Software,Dalian Neusoft Information University,Dalian 116023,China) |
| Abstract: |
| 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. |
| Key words: machine learning decision tree clustering attribute weight multi-way splits |
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