Pruning Method of Belief Decision Trees

被引：0

作者：

Trabelsi, Salsabil ^{[1
]}

Elouedi, Zied ^{[1
]}

Mellouli, Khaled ^{[1
]}

机构：

[1] Inst Super Gest Tunis, LARODEC, Le Bardo 2000, Tunisia

来源：

PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 14 | 2006年 / 14卷

关键词：

machine learning; uncertainty; belief function theory; belief decision tree; pruning;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning.

引用

页码：424 / 429

页数：6

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