Neuron Labeling for Self-Organizing Maps Using a Novel Example-Centric Algorithm with Weight-Centric Finalization

Willem van Heerden

2024

Abstract

A self-organizing map (SOM) is an unsupervised artificial neural network that models training data using a map structure of neurons, which preserves the local topological structure of the training data space. An important step in the use of SOMs for data science is the labeling of neurons, where supervised neuron labeling is commonly used in practice. Two widely-used supervised neuron labeling methods for SOMs are example-centric neuron labeling and weight-centric neuron labeling. Example-centric neuron labeling produces high-quality labels, but tends to leave many neurons unlabeled, thus potentially hampering the interpretation or use of the labeled SOM. Weight-centric neuron labeling guarantees a label for every neuron, but often produces less accurate labels. This research proposes a novel hybrid supervised neuron labeling algorithm, which initially performs example-centric neuron labeling, after which missing labels are filled in using a weight-centric approach. The objective of this algorithm is to produce high-quality labels while still guaranteeing labels for every neuron. An empirical investigation compares the performance of the novel hybrid approach to example-centric neuron labeling and weight-centric neuron labeling, and demonstrates the feasibility of the proposed algorithm.

Paper Citation

in Harvard Style

van Heerden W. (2024). Neuron Labeling for Self-Organizing Maps Using a Novel Example-Centric Algorithm with Weight-Centric Finalization. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 508-519. DOI: 10.5220/0013072000003837

in Bibtex Style

@conference{ncta24,
author={Willem van Heerden},
title={Neuron Labeling for Self-Organizing Maps Using a Novel Example-Centric Algorithm with Weight-Centric Finalization},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={508-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013072000003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Neuron Labeling for Self-Organizing Maps Using a Novel Example-Centric Algorithm with Weight-Centric Finalization
SN - 978-989-758-721-4
AU - van Heerden W.
PY - 2024
SP - 508
EP - 519
DO - 10.5220/0013072000003837
PB - SciTePress