A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification

Rodica Lung

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Paper

A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification

Topics: Evolutionary Learning Systems; Evolutionary Search and Meta-heuristics

In Proceedings of the 15th International Joint Conference on Computational Intelligence ECTA - Volume 1, 258-264, 2023 , Rome, Italy

Author: Rodica Lung

Affiliation: Center for the Study of Complexity, Babeş-Bolyai University, T. Mihali 58-60, Cluj Napoca, Romania

Keyword(s): Ensemble Learning, Classification, Game Theory, Differential Evolution.

Abstract: Aggregating results of several learners known to each perform well on different data types is a challenging task that requires finding intelligent, trade-off solutions. A simple game-theoretic approach to this problem is proposed. A non-cooperative game is used to aggregate the results of different classification methods. The Nash equilibrium of the game is approximated by using a Differential Evolution algorithm. Numerical experiments indicate the potential of the approach for a set of synthetic and real-world data.

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Paper citation in several formats:

Lung, R. (2023). A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification. In Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 258-264. DOI: 10.5220/0012192700003595

@conference{ecta23,
author={Rodica Lung.},
title={A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA},
year={2023},
pages={258-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012192700003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA
TI - A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification
SN - 978-989-758-674-3
IS - 2184-3236
AU - Lung, R.
PY - 2023
SP - 258
EP - 264
DO - 10.5220/0012192700003595
PB - SciTePress