AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Bhowan:2010:gecco,

• author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
• title = "AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data",
• booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation",
• year = "2010",
• editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt",
• isbn13 = "978-1-4503-0072-8",
• pages = "845--852",
• keywords = "genetic algorithms, genetic programming",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Portland, Oregon, USA",
• DOI = "doi:10.1145/1830483.1830639",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach using the accuracy of the minority and majority class as learning objectives. We focus our analysis on the classification ability of evolved Pareto-front solutions using the Area Under the ROC Curve (AUC) and investigate which regions of the objective trade-off surface favour high-scoring AUC solutions. We show that a diverse set of well-performing classifiers is simultaneously evolved along the Pareto-front using the MOGP approach compared to canonical GP where only one solution is found along the objective trade-off surface, and that in some problems the MOGP solutions had better AUC than solutions evolved with canonical GP using hand-crafted fitness functions.",
• notes = "Also known as \cite{1830639} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)",

}

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston

Citations