Structural risk minimization on decision trees using an evolutionary multiobjective optimization

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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
Pages (from-to)338-348
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3003
Publication statusPublished - 2004 Dec 1

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Evolutionary multiobjective Optimization
Decision trees
Multiobjective optimization
Decision tree
Data Classification
Information Gain
Genetic programming
Genetic Programming
Classification Problems
Error Rate
Learning systems
Machine Learning
Attribute

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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