Improving Decision Tree Performance by Exception Handling

被引：8

作者：

Subramanian A.A.B. ^{[1
]}

Pramala S. ^{[1
]}

Rajalakshmi B. ^{[1
]}

Rajaram R. ^{[2
]}

机构：

[1] Department of Information Technology, Thiagarajar College of Engineering, Madurai

[2] Department of Computer Science, Thiagarajar College of Engineering, Madurai

来源：

International Journal of Automation and Computing | 2010年 / 7卷 / 3期

关键词：

association rule mining (ARM); classification; Data mining; decision tree; k-nearest-neighbour (k-NN); majority voting; naive Bayes (NB);

D O I：

10.1007/s11633-010-0517-5

中图分类号：

学科分类号：

摘要：

This paper focuses on improving decision tree induction algorithms when a kind of tie appears during the rule generation procedure for specific training datasets. The tie occurs when there are equal proportions of the target class outcome in the leaf node's records that leads to a situation where majority voting cannot be applied. To solve the above mentioned exception, we propose to base the prediction of the result on the naive Bayes (NB) estimate, k-nearest neighbour (k-NN) and association rule mining (ARM). The other features used for splitting the parent nodes are also taken into consideration. © 2010 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

引用

页码：372 / 380

页数：8

共 24 条

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