@inproceedings{4d78bc15aa9b480a942cf5be52a02b52,
title = "Evolving data sets to highlight the performance differences between machine learning classifiers",
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).",
keywords = "Complexity measures, Evolutionary computation, Machine learning",
author = "Thomas Raway and Schaffer, {J. David} and Kurtz, {Kenneth J.} and Hiroki Sayama",
year = "2012",
doi = "10.1145/2330784.2330907",
language = "English",
isbn = "9781450311786",
series = "GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion",
publisher = "Association for Computing Machinery",
pages = "657--658",
booktitle = "GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion",
note = "14th International Conference on Genetic and Evolutionary Computation Companion, GECCO'12 Companion ; Conference date: 07-07-2012 Through 11-07-2012",
}