Illinois Genetic Algorithms Laboratory

Butz, M. V., Lanzi, P. L., Llorà, X., Goldberg, D. E. (2004)
TR No.: 2004024 | Download PDF | Download PS

Abstract:
XCS has been shown to solve hard problems in a machine-learning
competitive way. Recent theoretical advancements show that the
system can scale-up polynomially in the problem complexity and
problem size given the problem is a k-DNF with certain properties.
This paper addresses two major issues in XCS: (1) knowledge
extraction and (2) structure identification. Knowledge extraction
addresses the issue of mining problem knowledge from the final
solution developed by XCS. The goal is to identify most important
features in the problem and the dependencies among those features.
The extracted knowledge may not only be used for further data
mining, but may actually be re-fed into the system giving it
further competence in solving problems in which dependent
features, that is, building blocks, need to be processed
effectively. This paper proposes to extract a feature dependency
tree out of the developed rule-based problem representation of
XCS. The investigations herein focus on Boolean function problems.
The extension to nominal and real-valued features is discussed.