EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

1 Citation (Scopus)

Abstract

This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with robust and accurate feature importance quantification and more reliable mechanistic interpretation of feature importance for prediction problems using fuzzy sets. The toolkit was developed to address uncertainties in feature importance quantification and lack of trustworthy feature importance interpretation due to the diverse availability of machine learning algorithms, feature importance calculation methods, and dataset dependencies. EFI merges results from multiple machine learning models with different feature importance calculation approaches using data bootstrapping and decision fusion techniques, such as mean, majority voting and fuzzy logic. The main attributes of the EFI toolbox are: (i) automatic optimisation of ML algorithms, (ii) automatic computation of a set of feature importance coefficients from optimised ML algorithms and feature importance calculation techniques, (iii) automatic aggregation of importance coefficients using multiple decision fusion techniques, and (iv) fuzzy membership functions that show the importance of each feature to the prediction task. The key modules and functions of the toolbox are described, and a simple example of their application is presented using the popular Iris dataset.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science
Subtitle of host publication8th International Workshop, LOD 2022, Revised Selected Papers, Part 2
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Giovanni Giuffrida, Renato Umeton
PublisherSpringer
Pages249-264
Number of pages16
Volume13811
ISBN (Print)9783031258909
DOIs
Publication statusPublished - 2023
EventInternational Conference on Machine Learning, Optimization, and Data Science, 2022: held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022 - Certosa di Pontignano, Siena, Italy
Duration: 18 Sept 202222 Sept 2022
Conference number: 8th
https://lod2022.icas.cc/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume13811 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Machine Learning, Optimization, and Data Science, 2022
Abbreviated titleLOD 2022
Country/TerritoryItaly
CitySiena
Period18/09/2222/09/22
Internet address

Keywords

  • Decision fusion
  • Feature importance
  • Fuzzy logic
  • Interpretability
  • Machine learning interpretation
  • Responsible AI

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