Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network

Abstract

Time deposit has the characteristics of strong stability and low cost. It is a stable source of funds for banks. In this paper, S_Kohonen network is used to predict the success rate of fixed deposit in bank telephone marketing. Firstly, the output layer is added after the competition layer of unsupervised Kohonen network, which makes Kohonen network become S_Kohonen network with supervised learning. Because the improved S_Kohonen network is similar to other feedforward neural networks, each adjacent layer is connected by weights, and the initial weights are random, which easily leads to the unstable output of the network, and still has the disadvantage of relatively low prediction accuracy. Therefore, an improved whale optimization algorithm (IWOA) is proposed to optimize the weights between the input layer and the competition layer of S_Kohonen network. In this paper, the inertia weight of whale optimization algorithm is introduced into random factor on the basis of non-linear decline, and then the random search pattern of Levy flight is introduced into whale algorithm. Finally, the empirical results show that the improved S_Kohonen network can more intuitively represent the classification results of the network, and the classification accuracy of S_Kohonen network optimized by IWOA is significantly higher than that of S_Kohonen network optimized by GA , WOA and LWOA algorithm.