Abstract
In this paper, Multi-class classification using an Improved Multiobjective Simultaneous learning framework (MCIMSDC) 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 [1] in 2010. In [1], the multiple objective functions are utilized to simultaneously optimize the clustering and classification learning by employing Bayesian theory. In [1], the selection of learning parameter i.e., clusters membership degree uj(xi) is initially chosen at random due to which the number of iteration and training time achieve while obtaining the stable cluster center is comparatively higher, but here in the proposed methodology, the value of clusters membership degree uj(xi) is calculated on the basis of randomly initialized cluster centers. Thus, these cluster centers are updated and corresponding uj(xi) is calculated iteratively. Experimental results show that, this method improve the performance by significantly reducing the number of iterations and training time required to obtain the cluster center. The same is being verified with six benchmark datasets.
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Bharill, N., Tiwari, A. (2012). Multi-class Classification Using an Improved Multiobjective Simultaneous Learning Framework. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_75
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DOI: https://doi.org/10.1007/978-81-322-0491-6_75
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