Naive Bayes classification model improvement method based on attribute weighting

A Bayesian classification and attribute technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of classification accuracy and low efficiency, and achieve the effect of improving accuracy and efficiency

Inactive Publication Date: 2019-09-10
CHENGDU UNIV OF INFORMATION TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for improving the Naive Bayesian classification model based on attribute weighting, which effectively weakens the conditional independence assumption of the Naive Bayesian classification model through attribute weighting, and eliminates

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  • Naive Bayes classification model improvement method based on attribute weighting
  • Naive Bayes classification model improvement method based on attribute weighting
  • Naive Bayes classification model improvement method based on attribute weighting

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[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0040] see figure 1 As shown, the present invention is a method for improving a naive Bayesian classification model based on attribute weighting, comprising the following steps:

[0041] Step S1, data preprocessing: discretize continuous data according to Gaussian segmentation, convert all non-digital information into numbers, and then perform discretization processing;

[0042] Step S2. Calculating grouped Spearman coefficients: Perform attribute fusion for combi...

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Abstract

The invention discloses a naive Bayes classification model improvement method based on attribute weighting, and relates to the field of data processing and classification. The method comprises the following steps of S1, preprocessing the data; S2, calculating a grouping Spearman coefficient, removing the redundant attributes, and updating the data set; S3, solving the prior probability and the class condition probability of each class; S4, calculating a weighting coefficient of each attribute of the updated training set; and S5, performing classification according to the weighted improved model, and performing statistics on a classification result. According to the method, the conditional independence assumption of the naive Bayes classification model is effectively weakened through an attribute weighting mode, the redundant attributes are removed through the Spearman coefficient, and the accuracy and efficiency of the naive Bayes model are obviously improved through the improved model.

Description

technical field [0001] The invention belongs to the field of data processing and classification, in particular to a method for improving a naive Bayesian classification model based on attribute weighting. Background technique [0002] Naive Bayesian classification algorithm is the most classic classification method. However, due to the conditional independence of the algorithm itself and the fact that all attributes have the same impact on the results, the accuracy of the algorithm is low in actual use. At present, the improvement methods of Naive Bayesian algorithm mainly include: improving based on the algorithm itself, such as Bayesian network classification algorithm, double Bayesian classification algorithm, lazy Bayesian network classification algorithm, etc. These methods make Bayesian The accuracy rate and application scope of the classification algorithm have been improved; combined with other methods to improve the Naive Bayesian classification algorithm, there ar...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24155G06F18/214
Inventor 岳希唐孟轩唐聃高燕
Owner CHENGDU UNIV OF INFORMATION TECH
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