GA-based Rule Enhancement in Concept Learning

Jukka Hekanaho, Turku Center for Computer Science and Åbo Akademi University, Finland

We apply DOGMA, a GA-based theory revision system, to MDL-based rule enhancement in supervised concept learning. The system takes as input classification data and a rule-based classification theory, produced by some rule-based learner, and builds a second model of the data. The search for the new model is guided by a MDL-based complexity measure. The proposed methodology offers a partial solution both to the local minima trap of fast greedy learners, and to the time complexity problem of GA-based learners. As an example we show how the system improves rules produced by C4.5.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.