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Extreme Learning Machine for Bank Clients Classification

In this paper, a classification mode for commercial bank clients’ classification using the extreme learning machine (ELM) algorithm is proposed to study the commercial banks VIP loss. Firstly, we adopt the existing data sets of banks to train the ELM model; then, customer classification algorithm and its parameters are selected for classification purpose. Lastly, comparative analysis with existed methods are also compared, which showed that its advantages with the traditional gradient algorithm and other classification algorithm, which further indicate that ELM algorithm can not only overcome their drawbacks but also has faster learning rate, higher rate of accuracy, and better generalization.