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

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.

Automated summary

The paper introduces two prototype selection classifiers based on fuzzy logic, demonstrating their good performance and efficiency in dealing with class-imbalanced data.