An Adaptive Fuzzy Rough Neural Network and Its Application in Classification
提出一种自适应模糊粗糙神经网络模型，将模糊粗糙集与神经网络结合，通过反向传播自适应学习模糊相似关系，提升分类任务中的数据拟合能力，实验表明优于多数现有算法。
Fuzzy rough set theory is an important approach for analyzing data uncertainty. However, the model lacks adaptive learning capabilities and cannot fit labeled data effectively in classification tasks. This study aims to introduce an adaptive learning mechanism into fuzzy rough set theory to enhance its data-fitting capability. To this end, this study seamlessly integrates fuzzy rough set theory with neural networks and proposes a novel fuzzy rough neural network model. This model adaptively learns fuzzy similarity relations in rough set models using the backpropagation algorithm. The proposed network model comprises five layers: the input, membership, fuzzy lower approximation, fully connected, and output layers. The fuzzy similarity relations between the input samples and training samples are computed in the membership layer. These relations are utilized in the fuzzy lower approximation layer to describe the degree to which the samples belong to different classes. The fuzzy rough lower approximations of the input samples are finally fused in the fully connected layer using feature weight coefficients. In the backpropagation stage, the gradient of the objective function is used to correct the fuzzy similarity relations and feature weight coefficients. This study theoretically proved that the proposed fuzzy rough network has a generalized function approximation property and can approximate any decision function. Experimental analysis showed that the proposed method is effective and performs better than most of the existing state-of-the-art algorithms.