Predicting problem difficulty for genetic programming applied to data classification

Published in Conferences Papers
  1. Leonardo Trujillo, Yuliana Mart\'ınez, Edgar Galván López and Pierrick Legrand. Predicting problem difficulty for genetic programming applied to data classification. In GECCO. 2011, 1355-1362. BibTeX

    @inproceedings{DBLP:conf/gecco/TrujilloMLL11,
    	author = "Leonardo Trujillo and Yuliana Mart\'{\i}nez and Edgar Galv{\'a}n L{\'o}pez and Pierrick Legrand",
    	title = "Predicting problem difficulty for genetic programming applied to data classification",
    	booktitle = "GECCO",
    	year = 2011,
    	pages = "1355-1362",
    	ee = "http://doi.acm.org/10.1145/2001576.2001759",
    	crossref = "DBLP:conf/gecco/2011",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Natalio Krasnogor and Pier Luca Lanzi (eds.). 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011. ACM, 2011. BibTeX

    @proceedings{DBLP:conf/gecco/2011,
    	editor = "Natalio Krasnogor and Pier Luca Lanzi",
    	title = "13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011",
    	booktitle = "GECCO",
    	publisher = "ACM",
    	year = 2011,
    	isbn = "978-1-4503-0557-0",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

During the development of applied systems, an important problem that must be addressed is that of choosing the correct tools for a given domain or scenario. This general task has been addressed by the genetic programming (GP) community by attempting to determine the intrinsic difficulty that a problem poses for a GP search. This paper presents an approach to predict the performance of GP applied to data classification, one of themost common problems in computer science. The novelty of the proposal is to extract statistical descriptors and complexity descriptors of the problem data, and from these estimate the expected performance of a GP classifier. We derive two types of predictive models: linear regression models and symbolic regression models evolved with GP. The experimental results show that both approaches provide good estimates of classifier performance, using synthetic and real-world problems for validation. In conclusion, this paper shows that it is possible to accurately predict the expected performance of a GP classifier using a set of descriptors that characterize the problem data.

Published in
GECCO '11 Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pages 1355-1362
http://ulir.ul.ie/handle/10344/2880
Date of conference
12 - 16 July 2011
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