Efficient Exploration of the Rashomon Set of Rule-Set Models

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A4 Artikkeli konferenssijulkaisussa
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Date

2024-08-25

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Department of Computer Science

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Language

en

Pages

12

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KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 478-489

Abstract

Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule-set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.

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Publisher Copyright: © 2024 Copyright held by the owner/author(s). | openaire: EC/H2020/654024/EU//SoBigData

Keywords

interpretable machine learning, rashomon set, rule-based classification, scalable algorithms

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DOI

10.1145/3637528.3671818

Citation

Ciaperoni, M, Xiao, H & Gionis, A 2024, Efficient Exploration of the Rashomon Set of Rule-Set Models . in KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . ACM, pp. 478-489, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25/08/2024 . https://doi.org/10.1145/3637528.3671818

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https://urn.fi/URN:NBN:fi:aalto-202410166756

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[article-cris] Perustieteiden korkeakoulu / SCI