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Multi-Objective Evolutionary Rule-Based Classification with Categorical Data

The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem,...

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Autores principales: Jiménez, Fernando, Martínez, Carlos, Miralles-Pechuán, Luis, Sánchez, Gracia, Sciavicco, Guido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513209/
https://www.ncbi.nlm.nih.gov/pubmed/33265773
http://dx.doi.org/10.3390/e20090684
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author Jiménez, Fernando
Martínez, Carlos
Miralles-Pechuán, Luis
Sánchez, Gracia
Sciavicco, Guido
author_facet Jiménez, Fernando
Martínez, Carlos
Miralles-Pechuán, Luis
Sánchez, Gracia
Sciavicco, Guido
author_sort Jiménez, Fernando
collection PubMed
description The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models.
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spelling pubmed-75132092020-11-09 Multi-Objective Evolutionary Rule-Based Classification with Categorical Data Jiménez, Fernando Martínez, Carlos Miralles-Pechuán, Luis Sánchez, Gracia Sciavicco, Guido Entropy (Basel) Article The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models. MDPI 2018-09-07 /pmc/articles/PMC7513209/ /pubmed/33265773 http://dx.doi.org/10.3390/e20090684 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jiménez, Fernando
Martínez, Carlos
Miralles-Pechuán, Luis
Sánchez, Gracia
Sciavicco, Guido
Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title_full Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title_fullStr Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title_full_unstemmed Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title_short Multi-Objective Evolutionary Rule-Based Classification with Categorical Data
title_sort multi-objective evolutionary rule-based classification with categorical data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513209/
https://www.ncbi.nlm.nih.gov/pubmed/33265773
http://dx.doi.org/10.3390/e20090684
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