A Novel Fuzzy Hypersphere Neural Network Classifier Using Class Specific Clustering for Robust Pattern Classification

This paper introduces two novel class-specific fuzzy clustering algorithms: Mean-based Supervised Clustering (MSC) and Density-based Mean Supervised Clustering (DMSC). These algorithms are designed to construct the hidden layer of the Fuzzy Hypersphere Neural Network (FHNN) classifier, which is structured on the framework of the Radial Basis Function Neural Network (RBFNN). The FHNN classifier utilizes fuzzy sets as labeled pattern clusters in its hidden layer, with classes represented in the output layer formed by the aggregation of these fuzzy sets. An important characteristic of this classifier is its independence from tuning parameters. It meticulously determines centroids and radii for labeled clusters, consistently achieving 100% accuracy across any training set. The FHNN classifier effectively handles outliers and is robust to variations in data presentation, ensuring clear data visualization for users. During the creation of labeled clusters in the hidden layer, binary weight values are adjusted concurrently between the hidden and output layers. This study proposes the formation of fuzzy clusters with varying dimensions tailored to the dataset. The classifier architecture, rooted in the radial basis function neural network, achieves 100% training accuracy due to precise fuzzy cluster formation. Experimental comparisons with RBFNN and similar classifiers using sixteen benchmark datasets demonstrate the superiority of the proposed classifier in pattern recognition tasks.