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

Rodica Lung

2023

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.

Paper Citation

in Harvard Style

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 - Volume 1: ECTA; ISBN 978-989-758-674-3, SciTePress, pages 258-264. DOI: 10.5220/0012192700003595

in Bibtex Style

@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 - Volume 1: ECTA},
year={2023},
pages={258-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012192700003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification
SN - 978-989-758-674-3
AU - Lung R.
PY - 2023
SP - 258
EP - 264
DO - 10.5220/0012192700003595
PB - SciTePress