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

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... t 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.