Semi-Supervised Fuzzy C-Means for Regression

Gabriella Casalino; Giovanna Castellano; Corrado Mencar

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Paper

Semi-Supervised Fuzzy C-Means for Regression

Topics: Fuzzy Clustering, Classification and Pattern Recognition

In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: FCTA, 369-375, 2023 , Rome, Italy

Authors: Gabriella Casalino ; Giovanna Castellano and Corrado Mencar

Affiliation: Dept. of Computer Science, University of Bari Aldo Moro, Italy

Keyword(s): Fuzzy Clustering, Semi-Supervised Clustering, Regression, Discretization.

Abstract: We propose a method to perform regression on partially labeled data, which is based on SSFCM (Semi-Supervised Fuzzy C-Means), an algorithm for semi-supervised classification based on fuzzy clustering. The proposed method, called SSFCM-R, precedes the application of SSFCM with a relabeling module based on target discretization. After the application of SSFCM, regression is carried out according to one out of two possible schemes: (i) the output corresponds to the label of the closest cluster; (ii) the output is a linear combination of the cluster labels weighted by the membership degree of the input. Some experiments on synthetic data are reported to compare both approaches.

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Paper citation in several formats:

Casalino, G.; Castellano, G. and Mencar, C. (2023). Semi-Supervised Fuzzy C-Means for Regression. In Proceedings of the 15th International Joint Conference on Computational Intelligence - FCTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 369-375. DOI: 10.5220/0012195100003595

@conference{fcta23,
author={Gabriella Casalino. and Giovanna Castellano. and Corrado Mencar.},
title={Semi-Supervised Fuzzy C-Means for Regression},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - FCTA},
year={2023},
pages={369-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012195100003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - FCTA
TI - Semi-Supervised Fuzzy C-Means for Regression
SN - 978-989-758-674-3
IS - 2184-3236
AU - Casalino, G.
AU - Castellano, G.
AU - Mencar, C.
PY - 2023
SP - 369
EP - 375
DO - 10.5220/0012195100003595
PB - SciTePress