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

Jinu Joo; Jun Zhang; Jihoon Yang; Vasant Honavar

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

Jinu Joo, Jun Zhang, Jihoon Yang, Vasant Honavar

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	347-354
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3245
State	Published - Dec 1 2004

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Cite this

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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.",

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AU - Joo, Jinu

AU - Zhang, Jun

AU - Yang, Jihoon

AU - Honavar, Vasant

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N2 - 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.

AB - 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.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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