Research on eigenvalue selection method of power market credit evaluation based on non parametric Bayesian discriminant analysis and cluster analysis

This paper presents a set of nonparametric methods for selecting credit evaluation indicators under the condition of unknown index distribution, and applies the data of four power companies as samples for application analysis. The results show that through the first round of credit feature screening based on nonparametric Bayes discrimination and the second round of nonparametric clustering, this paper finally constructs a credit feature screening model based on non parametric Bayesian discriminant and clustering analysis, and carries out application analysis. Finally, 18 credit characteristics which can be used in power market credit evaluation are screened out, and the evaluation accuracy has been significantly improved from 73.64% to 77.02%. (C) 2021 The Author(s). Published by Elsevier Ltd.

Automated summary

This paper presents a nonparametric method for selecting credit evaluation indicators under unknown index distribution, and through two rounds of screening and analysis, 18 credit characteristics suitable for power market evaluation are identified, significantly improving evaluation accuracy.