FASTEN: Fuzzy Neural Support Vector Machine for Classification

Zhian Yuan, Yuhua Qian, Xinyan Liang, Yi Kou, Chenping Hou, Qinghua Hu

Abstract: Classification tasks have long been a central concern in the field of machine learning. Although deep neural network-based approaches offer a novel, versatile, and highly precise solution for classification tasks, the intrinsic ambiguity and uncertainty present in the data continue to pose a significant challenge, impeding the potential for further advancements in classification accuracy. To address this issue, we propose a fuzzy neural support vector machine (FASTEN), which mitigates the fuzziness and uncertainty inherent in the data and enhances the classification performance. FASTEN mainly consists of two parts: the fuzzy feature extraction unit and the multipath classifier aggregation unit. The fuzzy feature extraction unit is designed to precisely capture the fuzzy features of the uncertainty data by embedding membership functions and fuzzy rules within the neural network. In this process, fuzzy parameters are dynamically updated on a task-driven basis, ensuring that the model can adaptively handle data with different degrees of fuzziness. The multipath classifier aggregation unit is designed to integrate the outputs of multiple neural network classifiers through dynamic linear combination. Meanwhile, we introduce the maximum margin theory of support vector machines to optimize the feature representation and improve classification accuracy. Our experimental results show that FASTEN can improve the classification performance on image and signal datasets. Through ablation experiments, the contributions of each unit to performance enhancement have been further validated, thereby establishing a foundation for subsequent model optimization.

External IDs:dblp:journals/tfs/YuanQLKHH25