AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Bhowan:2010:gecco,
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author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
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title = "AUC analysis of the pareto-front using multi-objective
GP for classification with unbalanced data",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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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",
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isbn13 = "978-1-4503-0072-8",
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pages = "845--852",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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DOI = "doi:10.1145/1830483.1830639",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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