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

Abstract

• Two classification methods based on prototype selection are presented. • These methods are based on fuzzy similarity relations as a more flexible alternative. • Proposed methods demonstrate good performance on imbalanced datasets. • These methods allow instance reduction in the learning process. • Prototypes help explain and interpret events. 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 postoperative mediastinitis.