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Mobile application classifying method under imbalanced perception data

A technology for sensing data and mobile applications, applied in the field of mobile computing, can solve problems such as the imbalance of the two types of samples, and achieve the effect of robust and accurate inference services

Inactive Publication Date: 2014-01-22
WUXI TSINGHUA NAT LAB FOR INFORMATIONSCI & TECH INTERNET OF THINGS TECH CENT
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AI Technical Summary

Problems solved by technology

However, these unlabeled data are also unbalanced in the number of samples of the two classes, so they cannot directly facilitate classifier training

Method used

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  • Mobile application classifying method under imbalanced perception data
  • Mobile application classifying method under imbalanced perception data
  • Mobile application classifying method under imbalanced perception data

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Embodiment Construction

[0029] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0030] The basic framework of the method of the present invention is as follows: firstly, by sub-sampling, a data subset consistent with the number of positive samples is sampled from a large amount of negative labeled data; then the similarity between the unlabeled data and the labeled data features , perform similarity-based sampling on unlabeled data to generate unlabeled data subsets; on each data subset (including labeled and unlabeled data), use semi-supervised learning to train sub-classifiers. The final overall classifier is ensembled by multiple sub-classifiers.

[0031] The present invention mainly includes three steps: sub-sampling, similarity sampling and sub-classifier integration. Before introducing these core steps, we explain the symbols used in Table 1.

[0032] Table 1 Common symbols

[0033]

[0034] 1. Subsampling.

[0035] Su...

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Abstract

The invention provides a mobile application classifying method under imbalanced perception data. The mobile application classifying method comprises the steps that firstly, data subsets which are the same with positive-type samples in number are sampled from a large number of negative-type with-label data through secondary sampling; then, sampling based on similarity is conducted on non-label data by the utilization of the characteristic similarity between the non-label data and the with-label data so as to generate non-label data subsets; a sub-classifier is obtained on each with-label data subset and each non-label data subset through training by the utilization of the semi-supervised studying method; at last, a master classifier is formed by integrating the sub-classifiers. The mobile application classifying method under the imbalanced perception data has the advantages that the mobile application classifying method can be used for deduction of events, activities and backgrounds of current intelligent mobile phone applications so that the designed classifier can be adapted to the screen that the positive-type data in the actual perception data and the negative-type data in the actual perception data are imbalanced in number, and deduction service which is accurate in robustness is provided for mobile phone perception applications.

Description

technical field [0001] The invention belongs to the field of mobile computing, and in particular relates to a method for classifying mobile applications under unbalanced perception data. Background technique [0002] In recent years, smart phones have become increasingly popular. According to the statistics of IDC, a well-known international statistics company, the sales volume of smart phones reached more than 700 million units in 2012, an increase of 44.1% over the sales volume in 2011. On the other hand, the embedded sensors on smartphones are becoming more and more abundant, which enables smartphones to increase the ability to perceive the environment in multiple dimensions on the basis of continuously improving computing and communication capabilities. These conditions make mobile application development in full swing. [0003] There is an important component in a large number of mobile applications, called the inference module or classifier, which is responsible for e...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/904
Inventor 刘云浩张幸林杨铮马强
Owner WUXI TSINGHUA NAT LAB FOR INFORMATIONSCI & TECH INTERNET OF THINGS TECH CENT
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