An improved multiobjective simultaneous learning framework for designing a classifier

In this paper, an Improved Multiobjective Simultaneous learning framework for Designing a Classifier (IMSDC) is proposed. This learning algorithm is used to solve any multiclass classification problem. It is based on the framework proposed by Cai, Chen and Zhang in 2010. In, multiple objective functions are utilized to formulate the problem of clustering and classification by employing Bayesian theory. In, the selection of learning parameter i.e., clusters membership degree u_j (x_i) is initially chosen at random, but here in the proposed methodology, the value of clusters membership degree u_j (x_i) is calculated on the basis of randomly initialized cluster centers. 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 six benchmark datasets.