Design and Implementation of a Classifier based on Multiobjective Learning Framework with Fuzzy Clustering

In this paper, Design and Implementation of a Classifier based on Multiobjective Learning Framework with Fuzzy Clustering (DICMFC) is proposed. This learning algorithm is used to solve any multiclass classification problem. In this Kernalized Fuzzy C-Means (KFCM) algorithm is used for enhance the robustnesss 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 initially chosen at random, but here in the proposed methodology, the value of clusters membership degree is 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.