GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@InProceedings{conf/eurogp/DoucetteH08,

• title = "{GP} Classification under Imbalanced Data sets: Active Sub-sampling and {AUC} Approximation",
• author = "John Doucette and Malcolm I. Heywood",
• bibdate = "2008-04-15",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DoucetteH08",
• booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008",
• address = "Naples",
• month = "26-28 " # mar,
• publisher = "Springer",
• year = "2008",
• volume = "4971",
• editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino",
• pages = "266--277",
• series = "Lecture Notes in Computer Science",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-78670-2",
• DOI = "doi:10.1007/978-3-540-78671-9_23",
• abstract = "The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and 'robust' fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during training is often not appropriate for designing classifiers that are robust to degenerate classifier behavior. To this end we propose a 'Simple Active Learning Heuristic' (SALH) in which a subset of exemplars is sampled with uniform probability under a class balance enforcing rule for fitness evaluation. In addition, an efficient estimator for the Area Under the Curve (AUC) performance metric is assumed in the form of a modified Wilcoxon-Mann-Whitney (WMW) statistic. Performance is evaluated in terms of six representative UCI data sets and benchmarked against: canonical GP, SALH based GP, SALH and the modified WMW statistic, and deterministic classifiers (Naive Bayes and C4.5). The resulting SALH-WMW model is demonstrated to be both efficient and effective at providing solutions maximizing performance assessed in terms of AUC.",
• notes = "John A. Doucette PhD 2016 University of Waterloo (not GP?) https://uwspace.uwaterloo.ca/items/1a44cde4-f734-460f-a65e-31871e22212c

  Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008",

}

Genetic Programming entries for John A Doucette Malcolm Heywood

Citations