Abstract
Credit risk assessment is acting as a survival weapon in almost every financial institution. It involves an in-depth and sensitive analysis of various economic, social, demographic, and other pertinent data provided by the customers and about the customers for building a more accurate and robust electronic finance system. The classification problem is one of the primary concerns in the process of analyzing the gamut of data; however, its complexity has ignited us to use machine learning-based approaches. In this paper, radial basis function neural network (RBFNN) with particle swarm optimization (RBFNN + PSO) and improved particle swarm optimization tuned radial basis function neural network (RBFNN + IMPSO) learning algorithms have been studied and compared their effectiveness for credit risk assessment. The experimental findings draw a clear line between the proposed model and traditional learning algorithms. Moreover, the proposed method is very promising vis-à-vis of individual classifiers.
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Pandey, T.N., Giri, P.K., Jagadev, A.K. (2020). Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks. In: Dehuri, S., Mishra, B., Mallick, P., Cho, SB., Favorskaya, M. (eds) Biologically Inspired Techniques in Many-Criteria Decision Making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_3
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