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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Approximate and Exact Enumeration of Rule Models

March 15, 2023

Authors

Satoshi Hara

Osaka University


Masakazu Ishihata

Hokkaido University


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

AAAI Technical Track: Machine Learning

Downloads:

Download PDF

Abstract:

In machine learning, rule models are one of the most popular choices when model interpretability is the primary concern. Ordinary, a single model is obtained by solving an optimization problem, and the resulting model is interpreted as the one that best explains the data. In this study, instead of finding a single rule model, we propose algorithms for enumerating multiple rule models. Model enumeration is useful in practice when (i) users want to choose a model that is particularly suited to their task knowledge, or (ii) users want to obtain several possible mechanisms that could be underlying the data to use as hypotheses for further scientific studies. To this end, we propose two enumeration algorithms: an approximate algorithm and an exact algorithm. We prove that these algorithms can enumerate models in a descending order of their objective function values approximately and exactly. We then confirm our theoretical results through experiments on real-world data. We also show that, by using the proposed enumeration algorithms, we can find several different models of almost equal quality.

DOI:

10.1609/aaai.v32i1.11637


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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