Abstract
Multi-label classification deals with problems where each instance is associated with multiple labels at the same time. Various techniques exist to solve the multi-label classification problem. One such technique is ML-RBF (Multi-Label Radial Basis Function), which has proved to be quite efficient. However, to further enhance the performance of the ML-RBF for multi-label classification problem, we have proposed two new algorithms. The first proposed algorithm is named as fuzzy PSO based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with SVD (Singular Value Decomposition). Both the proposed algorithms are applied to real world datasets i.e. yeast and scene dataset. The experimental results show that both the proposed algorithms meets or beats ML-RBF when applied on the test datasets.
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Agrawal, J., Agrawal, S., Kaur, S., Sharma, S. (2014). An Investigation of Fuzzy PSO and Fuzzy SVD Based RBF Neural Network for Multi-label Classification. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_59
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DOI: https://doi.org/10.1007/978-81-322-1771-8_59
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