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

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

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

1 Citation (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
Pages657-658
Number of pages2
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 2012 Jul 72012 Jul 11

Other

Other14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period12/7/712/7/11

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Keywords

  • Complexity measures
  • Evolutionary computation
  • Machine learning

ASJC Scopus subject areas

  • Computational Theory and Mathematics

Cite this

Raway, T., Schaffer, J. D., Kurtz, K. J., & Sayama, H. (2012). Evolving data sets to highlight the performance differences between machine learning classifiers. In GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion (pp. 657-658) https://doi.org/10.1145/2330784.2330907