present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7â8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. Dixon et al. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. We have included in this volume revised and extended versions of the papers presented at the workshop.
Books > Computer Science
Learning Classifier Systems
Specifications of Learning Classifier Systems | |
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Category | Medien > Bücher |
Instock | instock |