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Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

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Abstract

We present a new classification method that uses genetic programming (GP) to evolve feature transformations for a deterministic, distanced-based classifier. This method, called M4GP, differs from common approaches to classifier representation in GP in that it does not enforce arbitrary decision boundaries and it allows individuals to produce multiple outputs via a stack-based GP system. In comparison to typical methods of classification, M4GP can be advantageous in its ability to produce readable models. We conduct a comprehensive study of M4GP, first in comparison to other GP classifiers, and then in comparison to six common machine learning classifiers. We conduct full hyper-parameter optimization for all of the methods on a suite of 16 biomedical data sets, ranging in size and difficulty. The results indicate that M4GP outperforms other GP methods for classification. M4GP performs competitively with other machine learning methods in terms of the accuracy of the produced models for most problems. M4GP also exhibits the ability to detect epistatic interactions better than the other methods.

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Notes

  1. 1.

    Source code available from http://github.com/lacava/ellyn.

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Acknowledgments

This work was supported by the Warren Center for Network and Data Science, as well as NIH grants P30-ES013508, AI116794 and LM009012. S. Silva acknowledges project PERSEIDS (PTDC/EMS-SIS/0642/2014) and BioISI RD unit, UID/MULTI/04046/2013, funded by FCT/MCTES/PIDDAC, Portugal. This material is based upon work supported by the National Science Foundation under Grants Nos. 1617087, 1129139 and 1331283. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    William La Cava & Jason Moore

  2. Faculdade de Ciências, Departamento de Informática, BioISI - Biosystems and Integrative Sciences Institute, Universidade de Lisboa, 1749-016, Lisboa, Portugal

    Sara Silva

  3. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Sara Silva

  4. NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Leonardo Vanneschi

  5. School of Cognitive Science, Hampshire College, Amherst, Massachusetts, USA

    Lee Spector

Authors
  1. William La Cava
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  2. Sara Silva
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  3. Leonardo Vanneschi
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  4. Lee Spector
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  5. Jason Moore
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Corresponding author

Correspondence to William La Cava .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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La Cava, W., Silva, S., Vanneschi, L., Spector, L., Moore, J. (2017). Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_11

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