Rochester model-naive Bayesian model-based data classification system

Abstract

The invention relates to a Rochester model-naive Bayesian model-based data classification system, which comprises a data processing module, a sampling module, a modeling module and a data testing module, wherein the data processing module divides an original sample set into a saturated layer and a lacking layer according to the input missing value ratio of each sample variable in the original sample set and relativity among the sample variables and sample attributes; the sampling module randomly extracts a training sample variable and a testing sample variable from the saturated layer and the lacking layer to form a training sample set and a testing sample set of which each comprises the saturated layer and the lacking layer respectively; the modeling module models training samples in the saturated layer through a Rochester regression model and models the training samples in the lacking layer through a naive Bayesian model to obtain a hybrid dynamic model with the Rochester regression model and the naive Bayesian model; and the data testing module inputs testing samples in the saturated layer into the Rochester regression model in the hybrid dynamic model, inputs the testing samples in the lacking layer into the naive Bayesian model in the hybrid dynamic model and performs a test to obtain and output scoring results. The Rochester model-naive Bayesian model-based data classification system is integrated with the functions of the Rochester regression model and the naive Bayesian model so as to have complementary advantages and can be widely applied to the financial industry, retailing and the telecommunication industry.

Description

technical field [0001] The invention relates to a data classification system, in particular to a data classification system based on a Rochester model-naive Bayesian model. Background technique [0002] Data mining is more and more widely used in the financial industry, retail industry and telecommunications industry. In the financial field, managers can use data mining to analyze, classify and grade customers' repayment ability and credit, thereby reducing the cost of issuing loans. Blindness, improve the ratio structure of bank assets and liabilities in various types of investment products, improve the efficiency of capital use, and optimize the asset structure. At the same time, it can also discover the leading factors and key links that play a decisive role in various capital operation businesses, so as to formulate corresponding financial policies. In the retail industry, data mining can help identify customer buying behavior, discover customer buying patterns and tren...

AI Technical Summary

Problems solved by technology

For the Rochester regression model, its advantages are mainly robustness, strong interpretability of the model, and a linear scorecard can be generated. The disadvantage is that the prediction accuracy is poor compared with some other systems, such as neural networks, Naive Bayes (naive Bayesian) model, etc.

The Naive Bayesian model is a simple and efficient classifier, but its variable independence assumption limits the application to actual data. Through modeling tests, it is found that the model established by using the Naive Bayesian model alone is very accurate. However, the robustness of the model is poor, and only the classification of part of the data is accurate

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