Curriculum Learning-Based Fuzzy Support Vector Machine

To improve the robustness of SVM models to noise and outliers, fuzzy support vector machine (FSVM) has been proposed. However, many existing FSVM models have limitations such as their dependence on assumptions, limited optimization, and unreasonable handling of noise. To address these problems, we propose a novel approach called curriculum learning-based FSVM. Our approach employs a curriculum-learning strategy, where the model initially learns easy samples to avoid noise interference and obtain a good initial solution, before proceeding to learn all samples, including hard ones. To distinguish between easy and hard samples, we introduce an adaptive density-based clustering model, which is extended to kernel feature space. Moreover, we propose a slack variable-based fuzzy membership function to evaluate the importance of samples. Additionally, our model adaptively adapts the importance of samples based on feedback during the learning process. Finally, our experimental results on popular benchmarks demonstrate that our proposed model outperforms existing competitors in terms of accuracy and robustness.