A Novel Robust Kernelized FCM Based Multi-Objective Simultaneous Learning Framework for Clustering and Classification

Saketh Innani, Pawan Chinnari, Soumen Sinha, Mehek Khan, Neha Bharill, Om Prakash Patel

Abstract: Clustering and classification are the two important tasks involved in pattern recognition. Both tasks are interrelated with each other. The generalization ability of classification learning can be enhanced with clustering results. On the contrary, the class information helps in improving the accuracy of clustering learning. Thus, both learning strategy complements each other. To amalgamate the benefits of both learning strategies, therefore in this paper, we proposed a novel robust kernelized Fuzzy c-Means based multi-objective simultaneous learning framework (RKFCM-MSCC) for both clustering and classification. RKFCM-MSCC employs multiple objective functions to compose the clustering and classification problem, respectively. Both the formulated objective functions are simultaneously optimized using the particle swarm optimization approach. Moreover RKFCM-MSCC uses Bayesian theory that make these multiple objective functions dependent on the single parameter i.e., cluster centers that connect both the clustering and classification learning. The Pareto-optimal solution attained with the RKFCM-MSCC approach complements the clustering and the classification learning process. The effectiveness of the proposed RKFCM-MSCC is empirically investigated on four benchmark datasets and the results are compared with the state-of-the-art approaches.