Using convex hulls to represent classifier conditions

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Using convex hulls to represent classifier conditions

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation table of contents

Seattle, Washington, USA

SESSION: Learning Classifier systems and other genetics-based machine learning: papers table of contents

Pages: 1481 - 1488

Year of Publication: 2006

ISBN:1-59593-186-4

Authors

Pier Luca Lanzi	Politecnico di Milano, Milano, Italy and University of Illinois at Urbana Champaign, Urbana, IL
Stewart W. Wilson	University of Illinois at Urbana Champaign, Urbana, IL

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 23, Citation Count: 1

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ABSTRACT

This papers presents a novel representation of classifier conditions based on convex hulls. A classifier condition is represented by a sets of points in the problem space. These points identify a convex hull that delineates a convex region in the problem space. The condition matches all the problem instances inside such region. XCSF with convex conditions is applied to function approximation problems and its performance is compared to that of XCSF with interval conditions. The comparison shows that XCSF with convex hulls converges faster than XCSF with interval conditions. However, convex conditions usually do not produce more compact solutions.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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