Evolving data sets to highlight the performance differences between machine learning classifiers

Thomas Raway*, J. David Schaffer, Kenneth J. Kurtz, Hiroki Sayama

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

We present a preliminary study to evolve data sets that maximize performance differences between multiple machine learning classifiers. The aim is to provide useful information towards the decision of which machine learning classifier to use given a particular data set. While literature already exists on comparing multiple classifiers across multiple pre-existing data sets, our approach is novel and unique in that we evolved completely new data sets designed to highlight the performance differences between supervised learning classifiers. By investigating these evolved data sets, we hope to add to the knowledge base concerning which classifiers are appropriate for specific real world classification tasks. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
PublisherAssociation for Computing Machinery
Pages657-658
Number of pages2
ISBN (Print)9781450311786
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event14th International Conference on Genetic and Evolutionary Computation Companion, GECCO'12 Companion - Philadelphia, PA, United States
Duration: 2012 Jul 72012 Jul 11

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Conference

Conference14th International Conference on Genetic and Evolutionary Computation Companion, GECCO'12 Companion
Country/TerritoryUnited States
CityPhiladelphia, PA
Period12/7/712/7/11

Keywords

  • Complexity measures
  • Evolutionary computation
  • Machine learning

ASJC Scopus subject areas

  • Computational Theory and Mathematics

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