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Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classi er System Jaume Bacardit Automated Scheduling, Optimisation and Planning group School of Computer Science and IT, University of Nottingham Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK jqb@cs.nott.ac.uk ABSTRACT This paper is focused on studying the initialization stage of learning classi er systems (LCS) applying the Pittsburgh approach. It has a theoretical part where the covering probability of a random rule set is modelled and a practical part. The practical part has the objective of developing general initialization policies that have competent performance on a broad range of datasets. Two kinds of policies are tested: (1) ways of tuning the initialization probability of the system and (2) smart initialization operators that create rules that are generalized versions of randomly sampled training instances. The results identify a subset of settings that are robust enough to be considered candidates to be the default initialization policy. These settings have competent performance compared to several alternative machine learning systems. Beside identifying the good policies, the experimentation made is also useful to give hints about what kind of initial solutions is the system able to process successfully to create well generalized solutions
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Analysis of the initialization stage of a Pittsburgh approach learning classifier system
Bacardit, Jaume
Association for Computing Machinery — Jun 25, 2005
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