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

Feature extraction and feature selection method oriented to background multi-source data

A feature selection method and feature extraction technology, applied in data processing applications, character and pattern recognition, instruments, etc., can solve problems such as the impact of prediction results

Inactive Publication Date: 2015-01-07
NANJING UNIV +1
View PDF2 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For LR classifiers, small changes in features will also have a great impact on the final prediction results

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 extraction and feature selection method oriented to background multi-source data
  • Feature extraction and feature selection method oriented to background multi-source data
  • Feature extraction and feature selection method oriented to background multi-source data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] Such as figure 1 As shown, for background multi-source data, specific feature extraction methods are used for different source data. For the extracted multi-source data features, the Group Lasso method is used for group feature selection, and a machine learning model is further established on the selected group features. Predict off-grid users.

[0030] Such as figure 2 As shown, the training set and test set are divided for the data from May 2013 to February 2014.

[0031] Such as image 3 As shown in the figure, it is a line chart of the daily online time of 50 users in May. The amount of data about users going online and offline is huge and contains a lot of information.

[0032] Such as Figure 4 , Figure 5 As shown, the method for extracting online time trend features based on multi-scale histogram statistics proposed by the present invention includes the following steps:

[0033] (1) This time series is not a typical time series in the traditional sense, ...

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

A feature extraction and feature selection method oriented to background multi-source data is characterized by including the following steps that first, background data of a plurality of months are divided into a training set and a testing set; second, corresponding grouping features are extracted on the training set according to different source data; third, feature group selection is performed on the testing set through cross validation with a Group Lasso method. The feature extraction and feature selection method oriented to background multi-source data has the advantages that a C45 strategy tree is used for the selected group feature so that a classifier off-network user analysis classifier can be established, the prediction accuracy on off-network users reaches 45%, and the prediction accuracy on downtime users with the off-network tendency reaches 88%.

Description

technical field [0001] The invention relates to a background multi-source data-oriented feature extraction and feature selection method for off-network user analysis. Background technique [0002] For each household's daily online time series, there is currently no good way to characterize the changing trend characteristics of users' online time. The Lasso method is a sparse feature selection method. When Lasso is directly applied to a model with a group structure, it tends to select a single feature and destroys the group structure of the feature. For LR classifiers, small changes in features will also have a great impact on the final prediction results. [0003] The Group Lasso method introduces the extension of the penalty function to study the selection of group features. The Filter method is a feature selection method that has nothing to do with the learning machine, and selects a subset of features through a certain measure. A commonly used measure is the Pearson co...

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
IPC IPC(8): G06K9/62G06K9/46
CPCG06Q10/04G06V10/50G06F18/24
Inventor 范剑锋杨琬琪高阳史颖欢孙良君
Owner NANJING 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