Title

Generalized Entropy For Splitting On Numerical Attributes In Decision Trees

Abstract

Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn patterns. The greedy search is based on the evaluation criterion on the candidate splits at each node. Although research has been performed on various such criteria, there is no significant improvement from the classical split approaches introduced in the early decision tree literature. This paper presents a new evaluation rule to determine candidate splits in decision tree classifiers. The experiments show that this new evaluation rule reduces the size of the resulting tree, while maintaining the trees accuracy.

Publication Date

7-24-2006

Publication Title

FLAIRS 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference

Volume

2006

Number of Pages

604-609

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

33746049801 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33746049801

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