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Feature Attribution Explanation Based on Multi-Objective Evolutionary Learning

Published: 01 August 2024 Publication History

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].

References

[1]
Umang Bhatt, Adrian Weller, and José MF Moura. 2021. Evaluating and aggregating feature-based model explanations. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. 3016--3022.
[2]
Arjun Chandra and Xin Yao. 2006. Ensemble learning using multi-objective evolutionary algorithms. Journal of Mathematical Modelling and Algorithms 5, 4 (2006), 417--445.
[3]
Kalyanmoy Deb and Himanshu Jain. 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2013), 577--601.
[4]
Changwu Huang, Zeqi Zhang, Bifei Mao, and Xin Yao. 2022. An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence 4, 4 (2022), 799--819.
[5]
Ziming Wang, Changwu Huang, Yun Li, and Xin Yao. 2024. Multi-objective Feature Attribution Explanation For Explainable Machine Learning. ACM Trans. Evol. Learn. Optim. 4, 1 (2024), 1--32.

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  1. Feature Attribution Explanation Based on Multi-Objective Evolutionary Learning

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    cover image ACM Conferences
    GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2024
    2187 pages
    ISBN:9798400704956
    DOI:10.1145/3638530
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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    Publication History

    Published: 01 August 2024

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    Author Tags

    1. explainable machine learning
    2. feature attribution explanations
    3. multi-objective learning
    4. multi-objective evolutionary algorithms

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