Efficient Exploration of the Rashomon Set of Rule-Set Models
2024 (English)In: KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM) , 2024, p. 478-489Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2024. p. 478-489
Keywords [en]
interpretable machine learning, rashomon set, rule-based classification, scalable algorithms
National Category
Computer Sciences Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-353966DOI: 10.1145/3637528.3671818ISI: 001324524200046Scopus ID: 2-s2.0-85203709623OAI: oai:DiVA.org:kth-353966DiVA, id: diva2:1901042
Conference
30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, Aug 25 2024 - Aug 29 2024
Note
Part of ISBN 9798400704901
QC 20240927
2024-09-252024-09-252025-03-17Bibliographically approved