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

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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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).

本文言語English
ホスト出版物のタイトルGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
ページ657-658
ページ数2
DOI
出版ステータスPublished - 2012 8 20
外部発表はい
イベント14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
継続期間: 2012 7 72012 7 11

出版物シリーズ

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

Other

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

ASJC Scopus subject areas

  • Computational Theory and Mathematics

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