Parameter-Free Undersampling for Multi-Label Data

Sarbani Palit, Payel Sadhukhan

2024

Abstract

This work presents a novel undersampling scheme to tackle the imbalance problem in multi-label datasets. We use the principles of the natural nearest neighborhood and follow a paradigm of label-specific undersam-pling. Natural-nearest neighborhood is a parameter-free principle. Our scheme’s novelty lies in exploring the parameter-optimization-free natural nearest neighborhood principles. The class imbalance problem is particularly challenging in a multi-label context, as the imbalance ratio and the majority-minority distributions vary from label to label. Consequently, the majority-minority class overlaps also vary across the labels. Working on this aspect, we propose a framework where a single natural neighbor search is sufficient to identify all the label-specific overlaps. Natural neighbor information is also used to find the key lattices of the majority class (which we do not undersample). The performance of the proposed method, NaNUML, indicates its ability to mitigate the class-imbalance issue in multi-label datasets to a considerable extent. We could also establish a statistically superior performance over other competing methods several times. An empirical study involving twelve real-world multi-label datasets, seven competing methods, and four evaluating metrics - shows that the proposed method effectively handles the class-imbalance issue in multi-label datasets. In this work, we have presented a novel label-specific undersampling scheme, NaNUML, for multi-label datasets. NaNUML is based on the parameter-free natural neighbor search and the key factor, neighborhood size ’k’ is determined without invoking any parameter optimization.

Paper Citation

in Harvard Style

Palit S. and Sadhukhan P. (2024). Parameter-Free Undersampling for Multi-Label Data. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 397-406. DOI: 10.5220/0012401400003636

in Bibtex Style

@conference{icaart24,
author={Sarbani Palit and Payel Sadhukhan},
title={Parameter-Free Undersampling for Multi-Label Data},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={397-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012401400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Parameter-Free Undersampling for Multi-Label Data
SN - 978-989-758-680-4
AU - Palit S.
AU - Sadhukhan P.
PY - 2024
SP - 397
EP - 406
DO - 10.5220/0012401400003636
PB - SciTePress