Simplify multi-valued decision trees

Chien-Liang Liu*, Chia-Hoang Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Decision tree is one of the popular data mining algorithms and it has been applied on many classification application areas. In many applications, the number of attribute values may be over hundreds and that will be difficult to analyze the result. The purpose of this paper will focus on the construction of categorical decision trees. A binary splitting decision tree algorithm is proposed to simplify the classification outcomes. It adopts the complement operation to simplify the split of interior nodes and it is suitable to apply on the decision trees where the number of outcomes is numerous. In addition, meta-attribute could be applied on some applications where the number of outcomes is numerous and the meta-attribute is meaningful. The benefit of meta-attribute representation is that it could transfer the original attributes into higher level concepts and that could reduce the number of outcomes.

Original languageEnglish
Title of host publicationAdvances in Computation and Intelligence - Third International Symposium, ISICA 2008, Proceedings
Number of pages10
StatePublished - 19 Dec 2008
Event3rd International Symposium on Intelligence Computation and Applications, ISICA 2008 - Wuhan, China
Duration: 19 Dec 200821 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5370 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Symposium on Intelligence Computation and Applications, ISICA 2008


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