Semi-hard clustering with application to classifier design - Comparisons with SVM on large data sets

Abstract

This paper discusses the application of the fuzzy c-means based classifier (FCMC) to large scale data sets. Large scale data sets contain a huge number of samples (patterns). The number can be reduced by sampling, but the accuracy of the classifier on the test set may deteriorate, and the accuracy on the available data worsens. The FCM classifier uses covariance matrices whose size does not increase with the number of training samples, and the training time is proportional to the number of samples. By comparing the performance of FCMC with the support vector machine (SVM) classifier, which is known as one of the highest performance classifiers, the paper shows that FCMC nearly attains the accuracy of SVM and surpasses it in the training time and the testing time.