Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations
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 -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 -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.
Cite
@article{arxiv.2302.04528,
title = {Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations},
author = {Chen Peng and Zhengqi Dai and Guangping Xia and Yajie Niu and Yihui Lei},
journal= {arXiv preprint arXiv:2302.04528},
year = {2023}
}