On tree-based classifications with multi-response variables

Seong Jun Kim, Hyunjoong Kim, Kang Bae Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Data mining has recently received much attention in a wide range of business and engineering field. Classification is one of the most important topics in data mining. Tree-based approach, for example decision tree, is a very useful technique for finding classification models. Most researches on decision trees have been conducted for the case of single response variable. However, situations where multi-response variables should be considered arise from many areas such as process monitoring, marketing science, and clinical analysis. This paper is concerned with tree-based classification methods when there are two or more response variables in the data set. We first give an overview of tree-based approaches and then provide three kinds of node splitting methods for the case in which multi-response variables are of concern. An illustrative example of tree-based classifications is also given with discussion.

Original languageEnglish
Pages (from-to)427-434
Number of pages8
JournalInternational Journal of Industrial Engineering : Theory Applications and Practice
Volume10
Issue number4
Publication statusPublished - 2003 Dec 1

Fingerprint

Decision trees
Data mining
Process monitoring
Marketing
Industry

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

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On tree-based classifications with multi-response variables. / Kim, Seong Jun; Kim, Hyunjoong; Lee, Kang Bae.

In: International Journal of Industrial Engineering : Theory Applications and Practice, Vol. 10, No. 4, 01.12.2003, p. 427-434.

Research output: Contribution to journalArticle

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