A Novel Metric for Measuring Data Quality in Classification Applications

Jouseau Roxane, Salva Sébastien, Samir Chafik

2024

Abstract

Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available observations. Indeed, without a clear understanding of the training and testing processes, it is hard to evaluate the intrinsic performance of a model. Besides, tools allowing to measure data quality specific to machine learning are still lacking. In this paper, we introduce and explain a novel metric to measure data quality. This metric is based on the correlated evolution between the classification performance and the deterioration of data. The proposed method has the major advantage of being model-independent. Furthermore, we provide an interpretation of each criterion and examples of assessment levels. We confirm the utility of the proposed metric with intensive numerical experiments and detail some illustrative cases with controlled and interpretable qualities.

Paper Citation

in Harvard Style

Roxane J., Sébastien S. and Chafik S. (2024). A Novel Metric for Measuring Data Quality in Classification Applications. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 141-148. DOI: 10.5220/0012311500003636

in Bibtex Style

@conference{icaart24,
author={Jouseau Roxane and Salva Sébastien and Samir Chafik},
title={A Novel Metric for Measuring Data Quality in Classification Applications},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012311500003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Novel Metric for Measuring Data Quality in Classification Applications
SN - 978-989-758-680-4
AU - Roxane J.
AU - Sébastien S.
AU - Chafik S.
PY - 2024
SP - 141
EP - 148
DO - 10.5220/0012311500003636
PB - SciTePress