Natarajan Meghanathan, et al. (Eds): SIPM, FCST, ITCA, WSE, ACSIT, CS & IT 06, pp. 349–356, 2012. © CS & IT-CSCP 2012 DOI : 10.5121/csit.2012.2334            Sachin Bhandari 1 and Dr. Aruna Tiwari 2 1 Department of Computer Engineering, SGSITS, Indore, India er.bhandari04@gmail.com 2 Department of Computer Engineering, SGSITS, Indore, India atiwari@sgsits.ac.in 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. KEYWORDS Semisupervised classification, Geometrical Expansion, Binary Neural Network, Fuzzy C- means algorithm, ETL algorithm. 1. INTRODUCTION Recently, the back propagation learning (BPL) algorithm has been applied to many binary-to- binary mapping problems [6], [2]. However, since the BPL algorithm searches the solution in continuous space, the BPL algorithm applied to binary-to-binary mapping problems results in long training time and inefficient performance. Typically, the BLTA algorithm require an extremely high number of iterations to obtain even a simple binary-to-binary mapping [3]. Also, in the BLTA algorithm, the number of neurons in the hidden layer required to solve a given problem is not known a priori. Since the numbers of neurons in the input and the output layer are determined by the dimensions of the input and output vectors, respectively, the abilities of three- layer neural networks depend on the number of neurons in the hidden layer. Therefore, one of the most important problems in application of three-layer neural networks is to determine the necessary number of neurons in the hidden layer. It has been widely recognized that Stone- Weierstrass’s theorem does not give a practical guideline in determining the required number of