Design and Implementation of Binary Neural Network Learning with Fuzzy Clustering

Abstract

In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the concept of binary neural network and geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. It's a semisupervised based approach, the training samples are semi-labelled i.e. for some samples, labels are known and for some samples data labels are not known. The method starts with classification, which is done by using the concept of ETL algorithm. In classification process various classes are formed. These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.