Feature weighting filter method based on correlation and Naive Bayes classification method

A Bayesian classification and feature weighting technology, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of sacrificing the computational complexity and simplicity of the model

Inactive Publication Date: 2017-02-08
CHINA UNIV OF GEOSCIENCES (WUHAN)
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The remaining methods, although taking into account both the correlation between feature variables and class variables and the redundancy between feature variables and feature variables, all sacrifice the computational complexity and simplicity of the model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Feature weighting filter method based on correlation and Naive Bayes classification method
  • Feature weighting filter method based on correlation and Naive Bayes classification method
  • Feature weighting filter method based on correlation and Naive Bayes classification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be further described below in conjunction with embodiment.

[0036] The invention provides a feature weighted filtering method based on correlation, comprising the following steps:

[0037] (1) Suppose A 1 ,A 2 ,...,A m Represents m feature variables of a known training instance set, a i Represents the characteristic variable A i The value of i∈[1,m]; C represents the class variable of the training instance, c represents the value of C, then for each feature variable A i , use the following formula to calculate the mutual information I(A i ;C):

[0038] I ( A i ; C ) = Σ a i Σ c P ( a i , c ) log P ( a ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention provides a feature weighting filter method based on correlation and a Naive Bayes classification method. The weight value of each feature variable is directly defined as the difference of the correlation of the feature variable and the class variable and the average redundancy of the feature variable and other feature variables. The present invention also provides a feature weighting Naive Bayes classification method depending on the method mentioned above. One group of obtained weighting values is substituted into the Naive Bayes classification formula for final classification of the test example. The feature weighting filter method based on correlation and the Naive Bayes classification method consider the correlation of the feature variable and the class variable and the redundancy between feature variables and maintain the model calculation complexity and the simplicity so as to verify the validity and the accuracy of the feature weighting method and the classification method provided by the invention through lots of experiment researches.

Description

technical field [0001] The invention relates to a correlation-based feature weighted filtering method and a naive Bayesian classification method, belonging to the technical field of artificial intelligence data mining classification. Background technique [0002] Suppose A 1 , A 2 ,...,A m are m feature variables, and a test instance x can be expressed as a feature vector<a 1 , a 2 ,...,a m >, where a i is the feature variable A i value of . Let C denote the class variable, c denote the value of C, and feature-weighted Naive Bayes uses Equation 1 to classify x. [0003] c ( x ) = arg max c ∈ C P ( c ) Π i = 1 m P ( a i | c ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24155
Inventor 蒋良孝张伦干李超群
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap