Abstract
Fuzzy c-Means based Classifier (FCMC) is a simple approach to classification based on the clustering and parameter optimization methods. In a classifier design, training of the classifier takes a long time when the size of the training set is very large. The training time of FCMC is improved by reducing memory usage and by revising the random search approach. This paper reports the results of the comparison between FCMC and the state of the art classifier: LibSVM. The number of clusters of FCMC is increased up to a maximum of 28=256. The two parameters out of four are automatically optimized by the revised random search approach. When the number of training samples is more than a million, the total training time for FCMC is estimated to be two to three orders of magnitude smaller than LibSVM, though FCMC achieves the same level of classification accuracy with LibSVM.
