Design a Classifier by Using Multi Objective Simultaneous Learning Framework with KFCM Algorithm

In this paper an approach ?Design a Classifier by Using Multiobjective Simultaneous Learning Framework with KFCM Algorithm (DCMK)? This learning algorithm is used to solve any multiclass classification problem. In this Kernalised Fuzzy C-Means (KFCM) algorithm is used for enhance the robustness of the classifier. It is based on the framework proposed by Cai, Chen and Zhang [1]. In [1], multiple objective functions are utilized to formulate the problem of clustering and classification by employing Bayesian theory. In [1], the clusters membership degree is u_j x_i initially chosen at random, but here in the proposed methodology, the value of clusters membership degree is u_j x_i calculated on the basis of randomly initialized cluster centers, these are the selection learning parameters. Experimental results show that, this method improve the performance by significantly reducing the number of iterations required to obtain the cluster center. The same is being verified with five benchmark datasets and compared with previous classifier.