Semi-Supervised Fuzzy C-Means for Regression

Gabriella Casalino, Giovanna Castellano, Corrado Mencar

2023

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.

Paper Citation

in Harvard Style

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 - Volume 1: FCTA; ISBN 978-989-758-674-3, SciTePress, pages 369-375. DOI: 10.5220/0012195100003595

in Bibtex Style

@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 - Volume 1: FCTA},
year={2023},
pages={369-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012195100003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: FCTA
TI - Semi-Supervised Fuzzy C-Means for Regression
SN - 978-989-758-674-3
AU - Casalino G.
AU - Castellano G.
AU - Mencar C.
PY - 2023
SP - 369
EP - 375
DO - 10.5220/0012195100003595
PB - SciTePress