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A meta-heuristic approach to estimate and explain classifier uncertainty

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posted on 2025-05-12, 09:54 authored by Andrew Houston, Georgina CosmaGeorgina Cosma

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model’s recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex mathematical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model’s decision-making process. This work proposes a set of class-independent meta-heuristics that can characterise the complexity of an instance in terms of factors that are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities and entropy-based methods of identifying instances at risk of being misclassified. Furthermore, the proposed approach resulted in uncertainty estimates that proves more independent of model accuracy and calibration than existing approaches. The proposed measures and framework demonstrate promise for improving model development for more complex instances and provides a new means of model abstention and explanation.

Funding

DMRC

Loughborough University

History

School

  • Science

Published in

Applied Intelligence

Volume

55

Issue

5

Publisher

Springer Nature

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acceptance date

2024-11-30

Publication date

2025-02-01

Copyright date

2025

ISSN

0924-669X

eISSN

1573-7497

Language

  • en

Depositor

Prof Georgina Cosma. Deposit date: 16 January 2025

Article number

319

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