Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fast feature selection method based on mixed-feature KDE conditional-entropy matrix formula

A feature selection method, a technique of mixing features, applied in the field of feature selection

Inactive Publication Date: 2017-11-24
TIANJIN UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the calculation time of KDE entropy is a major drawback of KDE entropy

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
  • Fast feature selection method based on mixed-feature KDE conditional-entropy matrix formula
  • Fast feature selection method based on mixed-feature KDE conditional-entropy matrix formula
  • Fast feature selection method based on mixed-feature KDE conditional-entropy matrix formula

Examples

Experimental program
Comparison scheme
Effect test

experiment example 1

[0136] By running the method of the present invention (abbreviated as FGS_KDE) on the actual data set wpbc (Breast-cancer-wisconsin3), two methods of discretizing continuous data are compared in terms of classification accuracy, one is equal-width discrete (Abbreviated to GS_eqW, the number of intervals parameter is 2, 4, 6), the other is equal frequency discrete (abbreviated to GS_eqF, the number of intervals parameter is 2, 4, 6). Among them, each method selects the best parameters. The results of the operation are shown in Table 1: Among them, the data set comes from the public UCI data warehouse (http: / / archive.ics.uci.edu / ml); the stop threshold T=0.01, h=k / log 2 n (k is 1, 2, 3), where n is the number of data samples. The classification accuracy is the average value of five-fold cross-validation, and the classifier used is KNN (k=3), JRip, C4.5. In terms of calculation speed, it compares the feature selection method based on the conditional entropy of mixed feature KDE (...

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 fast feature selection method based on the mixed feature KDE conditional entropy matrix, which proposes the concepts of kernel matrix, data vector, partition matrix and kernel partition matrix, and based on these concepts, proposes a mixed feature KDE The entropy matrix greatly improves the calculation efficiency of the mixed feature KDE conditional entropy, thereby improving the efficiency of the feature selection method based on the mixed feature KDE conditional entropy matrix.

Description

Technical field [0001] The present invention relates to a feature selection method, in particular to a fast feature selection method based on a mixed feature KDE conditional entropy matrix formula. Background technique [0002] Feature selection is an important preprocessing process in data mining, machine learning, pattern recognition and other fields. It performs feature selection on multi-dimensional data, but loses redundant and task-related features, which improves the efficiency of learning tasks. [0003] The feature selection method based on information theory is one of the effective methods in the feature selection method, but the traditional feature selection method based on information theory is only for discrete data. The feature selection algorithm based on KDE entropy utilizes the effectiveness of kernel density estimation (KDE) for the parameter-free estimation of continuous random variables, and effectively unifies the continuous attributes and discrete attributes i...

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): G06F17/16
CPCG06F17/16
Inventor 代建华高帅超徐思琪
Owner TIANJIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com