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

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.

• Type: Conference
• Year: 2004
• Where: DIS
• Authors: Jinu Joo, Jun Zhang 0002, Jihoon Yang, Vasant Honavar
• Keywords: Attribute Value Taxonomies, Decision Tree Learning, DIS 2004, Real World Datasets, Theoretical Computer Science