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Download e-book for kindle: Learning Classifier Systems: 11th International Workshop, by Jaume Bacardit, Will Browne, Jan Drugowitsch, Ester

By Jaume Bacardit, Will Browne, Jan Drugowitsch, Ester Bernadó-Mansilla, Martin V. Butz

ISBN-10: 3642175074

ISBN-13: 9783642175077

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Read or Download Learning Classifier Systems: 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, ... PDF

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Extra info for Learning Classifier Systems: 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, ...

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Support is computed as: suppi = cmi , ctime − tcreatei (6) where ctime is the time of the current iteration and tcreatei is the iteration in which the classifier i has been created. Then, the confidence is computed as confi = cmi . expi (7) Lastly, the fitness of each rule i in [M] is updated with the following formula Fi = (confi · suppi)ν , (8) where ν is a user-set parameter that permits controlling the pressure toward highly fit classifiers. Note that with this fitness computation, the system makes pressure towards the evolution of rules with not only high confidence but also high support.

Cattolico, M. ): Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, Washington, USA, July 8-12. ACM, New York (2006) 7. : Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books (1998) 8. : Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989) 9. : Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. ) Machine learning, an artificial intelligence approach, vol.

Note that, during learning, the subsumption mechanism requires that the confidence of ri be greater than conf 0 . After applying the rule set reduction mechanism, we make sure that the final population consists of different rules. Other policies can be easily incorporated to this process such as removing rules whose support and confidence are below a predefined threshold. Nonetheless, in our experiments we return all the experienced rules in the final population that are not subsumed by any other. The overall section has described the mechanisms that CSar uses to evolve a population of interesting association rules online.

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Learning Classifier Systems: 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, ... by Jaume Bacardit, Will Browne, Jan Drugowitsch, Ester Bernadó-Mansilla, Martin V. Butz


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