GA-based rule enhancement in concept learning
Jukka Hekanaho · 1997
We describe an application of 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, hopefully more accurate, model of the data. Unlike most theory revision systems DOGMA doesn't revise the initial rules, but builds instead a completely new theory, using stochastic sampling and adaptation of the initial rules. 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 rule-based concept learners, and to the time complexity problem of GA-based concept learners. As an example we show how the system improves rules produced by C4.5Rules. Keywords: Rule Enhancement, Concept Learning, Genetic Algorithms, Minimal Description Length 1 INTRODUCTION Genetic Algorithms (GAs) are stochastic and p...