Binary and multinomial classification through evolutionary symbolic regression

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf CartesianClf and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms - -XGBoost, LightGBM, and a deep neural network - -we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.

Original languageEnglish
Title of host publicationGECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages300-303
Number of pages4
ISBN (Electronic)9781450392686
DOIs
StatePublished - Jul 2022
Event2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States
Duration: 9 Jul 202213 Jul 2022

Publication series

NameGECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

Conference

Conference2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Country/TerritoryUnited States
CityVirtual, Online
Period9/07/2213/07/22

Keywords

  • classification
  • genetic programming
  • symbolic regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computational Mathematics
  • Theoretical Computer Science

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