Generating AVTs Using GA for Learning Decision Tree Classifiers with Missing Data

Jinu Joo, Jun Zhang, Jihoon Yang, Vasant Honavar

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Attribute value taxonomies (AVTs) have been used to perform AVT-guided decision tree learning on partially or totally missing data. In many cases, user-supplied AVTs are used. We propose an approach to automatically generate an AVT for a given dataset using a genetic algorithm. Experiments on real world datasets demonstrate the feasibility of our approach, generating AVTs which yield comparable performance (in terms of classification accuracy) to that with user supplied AVTs.

Original languageEnglish (US)
Pages (from-to)347-354
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3245
StatePublished - Dec 1 2004

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Taxonomies
Decision trees
Taxonomy
Missing Data
Decision tree
Classifiers
Classifier
Attribute
Learning
Gas
Genetic algorithms
Genetic Algorithm
Demonstrate
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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