Skip to main content
Kent Academic Repository

An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers

Cagnini, Henry E. L. and Freitas, Alex A. and Barros, Rodrigo C. (2020) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers. In: Cerri, Ricardo and Prati, Ronaldo C., eds. Intelligent Systems. 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I. Lecture Notes in Computer Science, 12319 . Springer, pp. 18-33. ISBN 978-3-030-61376-1. (doi:10.1007/978-3-030-61377-8_2) (KAR id:84754)


Ensembles of classifiers are a very popular type of method for performing classification, due to their usually high predictive accuracy. However, ensembles have two drawbacks. First, ensembles are usually considered a ‘black box’, non-interpretable type of classification model, mainly because typically there are a very large number of classifiers in the ensemble (and often each classifier in the ensemble is a black-box classifier by itself). This lack of interpretability is an important limitation in application domains where a model’s predictions should be carefully interpreted by users, like medicine, law, etc. Second, ensemble methods typically involve many hyper-parameters, and it is difficult for users to select the best settings for those hyper-parameters. In this work we propose an Evolutionary Algorithm (an Estimation of Distribution Algorithm) that addresses both these drawbacks. This algorithm optimizes the hyper-parameter settings of a small ensemble of 5 interpretable classifiers, which allows users to interpret each classifier. In our experiments, the ensembles learned by the proposed Evolutionary Algorithm achieved the same level of predictive accuracy as a well-known Random Forest ensemble, but with the benefit of learning interpretable models (unlike Random Forests).

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-61377-8_2
Uncontrolled keywords: Classification, Evolutionary algorithms, Ensemble learning, Machine learning, Supervised learning
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 11 Dec 2020 14:32 UTC
Last Modified: 12 Oct 2021 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.