Resumen de Financial customer classification by combined model
Yong Lin, Jinju Zheng
This paper explores the pros and cons of different algorithm models on the same selection problem, and then uses thecombined prediction theory to obtain a new combined prediction model to explore its prediction accuracy. The actualproblem to be solved is to help financial institutions to scientifically classify customers who choose financial products. Weselect the bank data set in the UCI database, which is derived from the survey data of a customer conducted by a financialinstitution in Portugal for a wealth management product. Decision tree C5.0 algorithm, naive Bayes classification algorithmand binary logit model are individually used to carry out a single model of empirical research on financial product customerclassification. Through the empirical analysis of the five combination models, it is concluded that in the model that usesthe least squares weighting method to determine the weight, the weight appears negative, which does not conform to theactual situation. The model that is based on the least squares weighting method and the model that is based on the simpleweighting method are excluded. In contrast, the arithmetic mean weighted model is better than the reciprocal varianceweighted model and the reciprocal mean square model. The accuracy reaches 89.91%, which is 0.43% higher than theaccuracy of a single model. It can be concluded that the model that is based on the arithmetic average weighting is a bettercombination forecasting model
