Cost-Sensitive Classification with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{li:2005:CECj,

• author = "Jin Li and Xiaoli Li and Xin Yao",
• title = "Cost-Sensitive Classification with Genetic Programming",
• booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation",
• year = "2005",
• editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer",
• volume = "3",
• pages = "2114--2121",
• address = "Edinburgh, UK",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "2-5 " # sep,
• organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7803-9363-5",
• URL = "http://www.cs.bham.ac.uk/~xin/papers/LiLiYaoCEC05.pdf",
• DOI = "doi:10.1109/CEC.2005.1554956",
• size = "8 pages",
• abstract = "Cost-sensitive classification is an attractive topic in data mining. Although genetic programming (GP) technique has been applied to general classification, to our knowledge, it has not been exploited to address cost-sensitive classification in the literature, where the costs of misclassification errors are non-uniform. To investigate the applicability of GP to cost-sensitive classification, this paper first reviews the existing methods of cost-sensitive classification in data mining. We then apply GP to address cost-sensitive classification by means of two methods through: a) manipulating training data, and b) modifying the learning algorithm. In particular, a constrained genetic programming (CGP), a GP based cost-sensitive classifier, has been introduced in this study. CGP is capable of building decision trees to minimise not only the expected number of errors, but also the expected misclassification costs through a novel constraint fitness function. CGP has been tested on the heart disease dataset and the German credit dataset from the UCI repository. Its efficacy with respect to cost has been demonstrated by comparisons with noncost-sensitive learning methods and cost-sensitive learning methods in terms of the costs.",
• notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.",

}

Genetic Programming entries for Jin Li Xiaoli Li Xin Yao

Citations