poster

Stochastic local search for efficient hybrid feature selection

Authors:

Ole Jakob Mengshoel,

Eirik FlogardAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 133 - 134

https://doi.org/10.1145/3449726.3459438

Published: 08 July 2021 Publication History

Abstract

There is a need to study not only accuracy but also computational cost in machine learning. Focusing on both accuracy and computational cost of feature selection, we develop and test stochastic local search (SLS) heuristics for hybrid feature selection.

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References

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Cited By

Sánchez-Díaz XMasson CMengshoel O(2024)Regularized Feature Selection Landscapes: An Empirical Study of MultimodalityParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_25(409-426)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_25
Flogard EMengshoel OKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)A dataset for efforts towards achieving the sustainable development goal of safe working environmentsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601963(23297-23310)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601963

Index Terms

Stochastic local search for efficient hybrid feature selection
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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Published In

cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 08 July 2021

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GECCO '21

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SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Sánchez-Díaz XMasson CMengshoel O(2024)Regularized Feature Selection Landscapes: An Empirical Study of MultimodalityParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_25(409-426)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_25
Flogard EMengshoel OKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)A dataset for efforts towards achieving the sustainable development goal of safe working environmentsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601963(23297-23310)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601963

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