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Feature importance in machine learning models: A fuzzy information fusion approach

Rengasamy, Divish; Mase, Jimiama M.; Kumar, Aayush; Rothwell, Benjamin; Torres, Mercedes Torres; Alexander, Morgan R.; Winkler, David A.; Figueredo, Grazziela P.

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Authors

Divish Rengasamy

Jimiama M. Mase

Aayush Kumar

Mercedes Torres Torres

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MORGAN ALEXANDER MORGAN.ALEXANDER@NOTTINGHAM.AC.UK
Professor of Biomedical Surfaces

David A. Winkler



Abstract

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpretation, there is an overall lack of consensus regarding how feature importance should be quantified, making explanations of model predictions unreliable. In addition, many of these explanations depend on the specific machine learning approach employed and on the subset of data used when calculating feature importance. A possible solution to improve the reliability of explanations is to combine results from multiple feature importance quantifiers from different machine learning approaches coupled with re-sampling. Current state-of-the-art ensemble feature importance fusion uses crisp techniques to fuse results from different approaches. There is, however, significant loss of information as these approaches are not context-aware and reduce several quantifiers to a single crisp output. More importantly, their representation of “importance” as coefficients may be difficult to comprehend by end-users and decision makers. Here we show how the use of fuzzy data fusion methods can overcome some of the important limitations of crisp fusion methods by making the importance of features easily understandable.

Citation

Rengasamy, D., Mase, J. M., Kumar, A., Rothwell, B., Torres, M. T., Alexander, M. R., …Figueredo, G. P. (2022). Feature importance in machine learning models: A fuzzy information fusion approach. Neurocomputing, 511, 163-174. https://doi.org/10.1016/j.neucom.2022.09.053

Journal Article Type Article
Acceptance Date Sep 4, 2022
Online Publication Date Sep 13, 2022
Publication Date Oct 28, 2022
Deposit Date Sep 16, 2022
Publicly Available Date Sep 16, 2022
Journal Neurocomputing
Print ISSN 0925-2312
Electronic ISSN 1872-8286
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 511
Pages 163-174
DOI https://doi.org/10.1016/j.neucom.2022.09.053
Keywords Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications
Public URL https://nottingham-repository.worktribe.com/output/11199698
Publisher URL https://www.sciencedirect.com/science/article/pii/S0925231222011584?via%3Dihub

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