Enhancement of the Classification Performance of Fuzzy C-Means With a Nonlinear Transformation Strategy for Data Structures
提出一种非线性变换策略，通过粒子群优化权重和支持向量回归构建变换模型，增强模糊C均值分类性能，在公开数据集上平均准确率提升16.029%。
Clustering provides a powerful technique for data analysis and data interpretation in the current complex background. Fuzzy clustering has gained significant attention in both research and applications due to its effectiveness in capturing the inherent uncertainty of real-world data. Among these methods, fuzzy C-means (FCM) stands out as one of the most representative and widely used approaches. This study develops a novel nonlinear transformation strategy to restructure data in order to improve the classification performance of FCM, and the transformed data structure, achieved through the developed nonlinear techniques, exhibits highly effective in enhancing the performance of FCM-based classifiers. In the proposed scheme, the original dataset is first partitioned into multiple subsets (matrix blocks) based on the original labels, with distinct weights assigned to each feature within these subsets. This process constructs a more separable dataset, referred to as the "expected high-performance dataset." Then, multiple nonlinear transformation models are constructed for the original dataset and for each feature of the constructed "expected high-performance dataset" with the support vector regression (SVR) method. During these operations, weight optimization is performed using particle swarm optimization (PSO), ultimately enhancing intraclass compactness by amplifying the similarity among samples within the same class. A comprehensive analysis of the proposed method was conducted, and experimental results on public datasets demonstrate its effectiveness and feasibility. The classification accuracy of the proposed method on multiple datasets has been improved by varying degrees compared with FCM, with an average improvement of 16.029% and a maximum improvement of 57.365%.