Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Classifier Systems

  • Pier Luca Lanzi
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_115



Classifier systems are rule-based systems that combine  temporal difference learning or  supervised learning with a genetic algorithm to solve classification and  reinforcement learning problems. Classifier systems come in two flavors: Michigan classifier systems, which are designed for online learning, but can also tackle offline problems; and Pittsburgh classifier systems, which can only be applied to offline learning.

In Michigan classifier systems (Holland, 1976), learning is viewed as an online adaptation process to an unknown environment that represents the problem and provides feedback in terms of a numerical reward. Michigan classifier systems maintain a single candidate solution consisting of a set of rules, or a population of classifiers. Michigan systems apply (1) temporal difference learning to distribute the incoming reward to the classifiers that are accountable for it; and (2) a genetic...

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  • Pier Luca Lanzi

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