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

Alvarez, Yanela Rodríguez; Lorenzo, María Matilde García; Mota, Yailé Caballero; Filiberto, Yaima; Hilarión, Isabel M. García; de, Daniela Machado Montes; Pérez, R.B.

doi:10.1016/j.patrec.2022.07.003

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…There have also been encouraging works for mitigating class imbalance. Alvarez et al [ 10 ] extend the capabilities of prototype based classifiers using fuzzy similarity relation to make them sensitive to class imbalanced data. GANS have also been used to generate synthetic data to help balance datasets [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Time to retire F1-binary score for action unit detection

Hinduja,

Nourivandi,

Cohn

et al. 2024

Pattern Recognition Letters

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Section: Introductionmentioning

confidence: 99%

Time to retire F1-binary score for action unit detection

Hinduja,

Nourivandi,

Cohn

et al. 2024

Pattern Recognition Letters

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“…When tackling imbalanced datasets, due to the main role of the majority class, the traditional classification methods designed for balanced datasets may not always achieve good classification performance for the minority class. Therefore, many improved classical algorithms and novel algorithms [12] have emerged to deal with imbalanced classification. As one of the most classic classification algorithms, SVM [13][14][15] shows relatively more robustness than other methods in imbalanced classification problems, but it is still unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

Zhou

Zhang

et al. 2022

Entropy

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The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.

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