Feature Attribution Explanation Based on Multi-Objective Evolutionary Learning

Ziming WANG, Changwu HUANG, Xin YAO*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

Feature attribution explanation (FAE) method, which reveals the contribution of each input feature to the model's output, is one of the most popular explainable artificial intelligence techniques. To assess the quality of explanations provided by FAE methods, various metrics spanning multiple dimensions have been introduced. However, current FAE approaches often prioritize faithfulness of their explanations, neglecting other crucial aspects. To address this issue, we define the construction of a FAE explainable model as a multi-objective learning problem and propose a framework that simultaneously considers multiple quality metrics during FAE explanation generation. Our experimental results demonstrate that our approach outperforms existing FAE methods in terms of faithfulness, sensitivity, and complexity. Moreover, our method has better diversity and the capacity to offer different explanations for different stakeholders.This paper for the Hot-off-the-Press track at GECCO 2024 summarizes the work Ziming Wang, Changwu Huang, Yun Li, and Xin Yao: Multi-objective Feature Attribution Explanation for Explainable Machine Learning published in ACM Transactions on Evolutionary Learning and Optimization [5].

Original languageEnglish
Title of host publicationGECCO '24 Companion: Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages61-62
Number of pages2
ISBN (Electronic)9798400704956
DOIs
Publication statusPublished - 1 Aug 2024
Event2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion - Melbourne, Australia
Duration: 14 Jul 202418 Jul 2024

Conference

Conference2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion
Country/TerritoryAustralia
CityMelbourne
Period14/07/2418/07/24

Bibliographical note

Publisher Copyright: © 2024 Copyright held by the owner/author(s).

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62250710682), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No.2017ZT07X386), and the Research Institute of Trustworthy Autonomous Systems.

Keywords

  • explainable machine learning
  • feature attribution explanations
  • multi-objective evolutionary algorithms
  • multi-objective learning

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