Fuzzy prototype selection-based classifiers for imbalanced data. Case study

doi:10.1016/j.patrec.2022.07.003

Fuzzy prototype selection-based classifiers for imbalanced data. Case study

2022-11-01

4

PRE

AI

Y

Yanela Rodríguez Álvarez

M

María Matilde García Lorenzo *

Isabel M. García Hilarión

D

Daniela Machado Montes de

R

Rafael Bello

DOI：10.1016/j.patrec.2022.07.003

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摘要

En 中文

Imbalanced data are popular in the machine learning community due to their likelihood of appearing in real-world application areas and the problems they present for classical classifiers. The goal of this work is to extend the capabilities of prototype-based classifiers using fuzzy similarity relations and to make them sensitive to class-imbalanced data classification. This paper proposes two new fuzzy logic -based prototype selection classifiers for imbalanced datasets, Imb-SPBASIR-Fuzzy_V1 (FPS-v1) and Imb-SPBASIR-Fuzzy_V2 (FPS-v1), and shows a comparative study of them with state-of-the-art methods on public datasets from the UCI machine learning repository. The results on the selected datasets suggest that fuzzy logic-based prototype selection classifiers perform well and efficiently, indicating that it is a viable alternative. The fuzzy relationships provided by this approach allow better results than the state-of-the-art models. Further analysis showed that the proposed fuzzy-based prototypes methods permit obtaining more accurate to deal with the correct prophylaxis, timely diagnosis and treatment of postop-erative mediastinitis.(c) 2022 Elsevier B.V. All rights reserved.

Keyword:

Fuzzy learning

Prototype classifiers

Imbalanced Data

Fuzzy prototype selection-based classifiers for imbalanced data. Case study

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