Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations

• URL: http://arxiv.org/abs/2302.04528v1
• Date: Thu, 9 Feb 2023 09:40:30 GMT
• Title: Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations
• Authors: Chen Peng, Zhengqi Dai, Guangping Xia, Yajie Niu, Yihui Lei
• Abstract summary: $q$-consistent summary-explanation aims to achieve greater support at the cost of slightly lower consistency. The challenge is that the max-support problem of $q$-consistent summary-explanation (MSqC) is much more complex than the original MS problem. To improve the solution time efficiency, this paper proposes the weighted column sampling(WCS) method.
• Score: 1.6262731094866383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability of decisions is crucial for users to develop trust on these systems. In recent years, the globally-consistent rule-based summary-explanation and its max-support (MS) problem have been proposed, which can provide explanations for particular decisions along with useful statistics of the dataset. However, globally-consistent summary-explanations with limited complexity typically have small supports, if there are any. In this paper, we propose a relaxed version of summary-explanation, i.e., the $q$-consistent summary-explanation, which aims to achieve greater support at the cost of slightly lower consistency. The challenge is that the max-support problem of $q$-consistent summary-explanation (MSqC) is much more complex than the original MS problem, resulting in over-extended solution time using standard branch-and-bound solvers. To improve the solution time efficiency, this paper proposes the weighted column sampling~(WCS) method based on solving smaller problems by sampling variables according to their simplified increase support (SIS) values. Experiments verify that solving MSqC with the proposed SIS-based WCS method is not only more scalable in efficiency, but also yields solutions with greater support and better global extrapolation effectiveness.

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