Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks

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-a-vis of individual classifiers.

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