Interpretable decision trees through MaxSAT

We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperf...

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Detalles Bibliográficos
Autores principales: Alòs, Josep, Ansótegui, Carlos, Torres, Eduard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794111/
https://www.ncbi.nlm.nih.gov/pubmed/36590759
http://dx.doi.org/10.1007/s10462-022-10377-0
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author Alòs, Josep
Ansótegui, Carlos
Torres, Eduard
author_facet Alòs, Josep
Ansótegui, Carlos
Torres, Eduard
author_sort Alòs, Josep
collection PubMed
description We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn.
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spelling pubmed-97941112022-12-27 Interpretable decision trees through MaxSAT Alòs, Josep Ansótegui, Carlos Torres, Eduard Artif Intell Rev Article We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn. Springer Netherlands 2022-12-27 /pmc/articles/PMC9794111/ /pubmed/36590759 http://dx.doi.org/10.1007/s10462-022-10377-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Alòs, Josep
Ansótegui, Carlos
Torres, Eduard
Interpretable decision trees through MaxSAT
title Interpretable decision trees through MaxSAT
title_full Interpretable decision trees through MaxSAT
title_fullStr Interpretable decision trees through MaxSAT
title_full_unstemmed Interpretable decision trees through MaxSAT
title_short Interpretable decision trees through MaxSAT
title_sort interpretable decision trees through maxsat
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794111/
https://www.ncbi.nlm.nih.gov/pubmed/36590759
http://dx.doi.org/10.1007/s10462-022-10377-0
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