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)|
|Number of pages||8|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Dec 1 2004|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)