Genetic programming representations for multi-dimensional feature learning in biomedical classification

William La Cava, Leonardo Vanneschi, Lee Spector, Jason Moore, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
PublisherSpringer-Verlag
Pages158-173
Number of pages16
Volume10199 LNCS
ISBN (Print)9783319558486
DOIs
Publication statusPublished - 2017
Event20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017 - Amsterdam, Netherlands
Duration: 19 Apr 201721 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10199 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
CountryNetherlands
City Amsterdam
Period19/04/1721/04/17

Keywords

  • Classification
  • Feature learning
  • Genetic programming

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