Evolving Gaussian Mixture Models for Classification

Simon Reichhuber, Sven Tomforde

2022

Abstract

The combination of Gaussian Mixture Models and the Expectation Maximisation algorithm is a powerful tool for clustering tasks. Although there are extensions for the classification task, the success of the approaches is limited, in part because of instabilities in the initialisation method, as it requires a large number of statistical tests. To circumvent this, we propose an ’evolutionary Gaussian Mixture Model’ for classification, where a statistical sample of models evolves to a stable solution. Experiments in the domain of Human Activity Recognition are conducted to demonstrate the sensibility of the proposed technique and compare the performance to SVM-based or LSTM-based approaches.

Paper Citation

in Harvard Style

Reichhuber S. and Tomforde S. (2022). Evolving Gaussian Mixture Models for Classification. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 964-974. DOI: 10.5220/0010984900003116

in Bibtex Style

@conference{icaart22,
author={Simon Reichhuber and Sven Tomforde},
title={Evolving Gaussian Mixture Models for Classification},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={964-974},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010984900003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Evolving Gaussian Mixture Models for Classification
SN - 978-989-758-547-0
AU - Reichhuber S.
AU - Tomforde S.
PY - 2022
SP - 964
EP - 974
DO - 10.5220/0010984900003116