Structural risk minimization on decision trees using an evolutionary multiobjective optimization

Research output: Chapter in Book/Report/Conference proceedingChapter

19 Citations (Scopus)

Abstract

Inducing decision trees is a popular method in machine learning. The information gain computed for each attribute and its threshold helps finding a small number of rules for data classification. However, there has been little research on how many rules are appropriate for a given set of data. In this paper, an evolutionary multi-objective optimization approach with genetic programming will be applied to the data classification problem in order to find the minimum error rate for each size of decision trees. Following structural risk minimization suggested by Vapnik, we can determine a desirable number of rules with the best generalization performance. A hierarchy of decision trees for classification performance can be provided and it is compared with C4.5 application.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMaarten Keijzer, Simon M. Lucas, Ernesto Costa, Terence Soule, Una-May O’Reilly
PublisherSpringer Verlag
Pages338-348
Number of pages11
ISBN (Print)3540213465, 9783540213468
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3003
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, D. E. (2004). Structural risk minimization on decision trees using an evolutionary multiobjective optimization. In M. Keijzer, S. M. Lucas, E. Costa, T. Soule, & U-M. O’Reilly (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 338-348). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3003). Springer Verlag. https://doi.org/10.1007/978-3-540-24650-3_32