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Continuous Dynamic Update of Fuzzy Random Forests

  • Datos identificativos

    Identificador: imarina:9281797
    Autores:
    Pascual-Fontanilles, JordiValls, AidaMoreno, AntonioRomero-Aroca, Pedro
    Resumen:
    Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.
  • Otros:

    Autor según el artículo: Pascual-Fontanilles, Jordi; Valls, Aida; Moreno, Antonio; Romero-Aroca, Pedro;
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Moreno Ribas, Antonio / Pascual Fontanilles, Jordi / Romero Aroca, Pedro / Valls Mateu, Aïda
    Palabras clave: Random forest Induction Fuzzy sets Dynamic learning models Decision tree Classification methods
    Resumen: Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.
    Áreas temáticas: Interdisciplinar General computer science Engenharias iv Computer science, interdisciplinary applications Computer science, artificial intelligence Computer science (miscellaneous) Computer science (all) Computational mathematics Ciência da computação
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: jordi.pascual@urv.cat jordi.pascual@urv.cat antonio.moreno@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
    Identificador del autor: 0000-0002-7528-5819 0000-0002-7528-5819 0000-0003-3945-2314 0000-0002-7061-8987 0000-0003-3616-7809
    Fecha de alta del registro: 2024-09-07
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: International Journal Of Computational Intelligence Systems. 15 (1):
    Referencia de l'ítem segons les normes APA: Pascual-Fontanilles, Jordi; Valls, Aida; Moreno, Antonio; Romero-Aroca, Pedro; (2022). Continuous Dynamic Update of Fuzzy Random Forests. International Journal Of Computational Intelligence Systems, 15(1), -. DOI: 10.1007/s44196-022-00134-0
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computational Mathematics,Computer Science (Miscellaneous),Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications
    Random forest
    Induction
    Fuzzy sets
    Dynamic learning models
    Decision tree
    Classification methods
    Interdisciplinar
    General computer science
    Engenharias iv
    Computer science, interdisciplinary applications
    Computer science, artificial intelligence
    Computer science (miscellaneous)
    Computer science (all)
    Computational mathematics
    Ciência da computação
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