Data classification improvement method based on naive Bayesian model

Abstract

The invention discloses a data classification improvement method based on a naive Bayes model. The data classification improvement method comprises the following steps: step 1, determining an intrusion detection method based on improved weighted hidden naive Bayes; 2, data processing is conducted on the collected data. Meanwhile, attribute weights are determined, and a basic model is established; 3, performing an attribute selection algorithm and data discretization in data processing; and 4, after the attribute weights are determined, weighting coefficients are metered. Meanwhile, the basic model is expanded. According to the method, the naive Bayesian model is optimized from the aspect of attribute selection. For the'conditional independence hypothesis' of naive Bayes, attributes can be selected to obtain an optimal attribute subset, so that the overall correlation among the attributes in the attribute subset is minimum. In this way, a part of influences brought by'conditional independence hypothesis' can be counteracted.

Description

technical field [0001] The invention relates to the technical field of improved data classification methods, and more specifically relates to an improved data classification method based on a naive Bayesian model. Background technique [0002] Naive Bayesian method is a classification method based on Bayesian theorem and the independent assumption of feature conditions. The two most widely used classification models are the Decision Tree Model and the Naive Bayesian Model (NBM). Compared with the decision tree model, the Naive Bayes Classifier (Naive Bayes Classifier or NBC) originated from classical mathematical theory, has a solid mathematical foundation, and stable classification efficiency. At the same time, the NBC model needs to estimate few parameters, is less sensitive to missing data, and the algorithm is relatively simple. In theory, the NBC model has the smallest error rate compared to other classification methods. But this is not always the case, because the N...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an improved data classification method based on the naive Bayesian model, which solves the problem that with the advent of the era of big data, the types of data actually collected are becoming more and more diverse. Study irrelevant or redundant attributes, which will have some negative impact on the classification results

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