A1 Journal article (refereed)
Comparison of feature importance measures as explanations for classification models (2021)


Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3(2), Article 272. https://doi.org/10.1007/s42452-021-04148-9


JYU authors or editors


Publication details

All authors or editorsSaarela, Mirka; Jauhiainen, Susanne

Journal or seriesSN Applied Sciences

ISSN2523-3963

eISSN2523-3971

Publication year2021

Volume3

Issue number2

Article number272

PublisherSpringer

Publication countryGermany

Publication languageEnglish

DOIhttps://doi.org/10.1007/s42452-021-04148-9

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/74012


Abstract

Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer data from the UCI Archive and a recently collected running injury data. Our results show that the most important features differ depending on the technique. We argue that a combination of several explanation techniques could provide more reliable and trustworthy results. In particular, local explanations should be used in the most critical cases such as false negatives.


Keywordsmachine learningartificial intelligenceclassification

Free keywordsfeature importance; explainable artificial intelligence; interpretable models; random forest; logistic regression


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Ministry reportingYes

Reporting Year2021

JUFO rating1


Last updated on 2024-10-03 at 20:06