Article

Feature selection and classification in noisy epistatic problems using a hybrid evolutionary approach

Authors:

Drew DeHaas,

Jesse Craig,

Colin Rickert,

Margaret J. Eppstein,

Paul Haake,

Kirsten StorAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Page 1872

https://doi.org/10.1145/1276958.1277331

Published: 07 July 2007 Publication History

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Abstract

A hybrid evolutionary approach is proposed for the combined problem of feature selection (using a genetic algorithm with Intersection/Union recombination and a fitness function based on a counter-propagation artificial neural network) and subsequent classifier construction (using strongly-typed genetic programming), for use in nonlinear association studies with relatively large potential feature sets and noisy class data. The method was tested using synthetic data with various degrees of injected noise, based on a proposed mental health database.allResults show the algorithm has good potential for feature selection, classification and function characterization.

Cited By

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Thieme ABelgrave DDoherty G(2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3398069
Hanley JEppstein MBuzas JRizzo DNeumann FSutton A(2016)Evolving Probabilistically Significant Epistatic Classification Rules for Heterogeneous Big DatasetsProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908931(445-452)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908931
Krupa JChatterjee SEldridge ERizzo DEppstein MMoore J(2012)Evolutionary feature selection for classificationProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330317(1111-1118)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330317

Index Terms

Feature selection and classification in noisy epistatic problems using a hybrid evolutionary approach
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Information & Contributors

Information

Published In

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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machine learning

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GECCO07

Sponsor:

GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

Acceptance Rates

GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Thieme ABelgrave DDoherty G(2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3398069
Hanley JEppstein MBuzas JRizzo DNeumann FSutton A(2016)Evolving Probabilistically Significant Epistatic Classification Rules for Heterogeneous Big DatasetsProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908931(445-452)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908931
Krupa JChatterjee SEldridge ERizzo DEppstein MMoore J(2012)Evolutionary feature selection for classificationProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330317(1111-1118)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330317

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Evolutionary feature selection: a novel wrapper feature selection architecture based on evolutionary strategies

Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems

Hybrid evolutionary algorithms for large scale continuous problems