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Feature Importance in Machine Learning Models: A Fuzzy Information Fusion Approach

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journal contribution
posted on 2022-10-27, 02:40 authored by D Rengasamy, JM Mase, A Kumar, B Rothwell, M Torres Torres, MR Alexander, David WinklerDavid Winkler, GP Figueredo

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. 

History

Publication Date

2022-10-01

Journal

Neurocomputing

Volume

511

Pagination

163 - 174

Publisher

Elsevier

ISSN

0925-2312

Rights Statement

© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).