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Soft Computing in Industrial Applications pp 159–168Cite as

Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality

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Part of the book series: Advances in Soft Computing ((AINSC,volume 39))

Abstract

We present a multi-dimensional mapping strategy using multiobjective genetic programming (MOGP) to search for the (near-)optimal feature extraction pre-processing stages for pattern classification as well as optimizing the dimensionality of the decision space. We search for the set of mappings with optimal dimensionality to project the input space into a decision space with maximized class separability. The steady-state Pareto converging genetic programming (PCGP) has been used to implement this multi-dimensional MOGP. We examine the proposed method using eight benchmark datasets from the UCI database and the Statlog project to make quantitative comparison with conventional classifiers. We conclude that MMOGP outperforms the comparator classifiers due to its optimized feature extraction process.

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Author information

Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 3JD, UK

    Yang Zhang & Peter I Rockett

Authors
  1. Yang Zhang
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  2. Peter I Rockett
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Editor information

Ashraf Saad Keshav Dahal Muhammad Sarfraz Rajkumar Roy

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© 2007 Springer-Verlag Berlin Heidelberg

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Zhang, Y., Rockett, P.I. (2007). Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality. In: Saad, A., Dahal, K., Sarfraz, M., Roy, R. (eds) Soft Computing in Industrial Applications. Advances in Soft Computing, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70706-6_15

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  • DOI: https://doi.org/10.1007/978-3-540-70706-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70704-2

  • Online ISBN: 978-3-540-70706-6

  • eBook Packages: EngineeringEngineering (R0)

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