A new fuzzy learning vector quantization method for classification problems based on a granular approach

Abstract

In this paper, a new Fuzzy Learning Vector Quantization (FuzzLVQ) method for classification is presented. FuzzLVQ is a hybrid method based on LVQ neural networks and fuzzy systems. FuzzLVQ was implemented using modular architectures based on a granular approach, to further improve its performance in complex classification problems. The contribution of this research work is the development of the new fuzzy learning quantization method (FuzzLVQ) for classification problems, which is a hybrid method based on LVQ neural networks and fuzzy inference systems. In this work, a set of 15 experiments were performed for each presented architecture and a comparison between the classical LVQ algorithm and the new FuzzLVQ is presented. The obtained results are favorable, and the classification accuracy is slightly higher with the FuzzLVQ method. The hybridization proved to be beneficial also in other aspects of the method, such as in the training times of the neural network, and the number of cluster centers, which are reduced in respect to the classical LVQ performance. A proper optimization method could help improve the classification accuracy even more.