Feature extraction method, device and server, and computer readable memory medium

A feature extraction and feature set technology, applied in the field of mobile Internet, can solve the problems of consumption, large number of feature bases, large memory resources, etc.

Active Publication Date: 2019-03-01
ZTE CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a feature extraction method, device, server, and computer-readable storage medium to solve the problem of large amount of calculations related to two features in the feature extraction of wireless side and user side data in mobile networks and The technical problem of consuming huge memory resources due to the large number of feature bases

Method used

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  • Feature extraction method, device and server, and computer readable memory medium
  • Feature extraction method, device and server, and computer readable memory medium
  • Feature extraction method, device and server, and computer readable memory medium

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Experimental program
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Embodiment 1

[0076] Such as figure 2 As shown, an embodiment of the present invention provides a feature extraction method, which includes:

[0077] S201: Perform feature pre-grouping on the pre-training data set on the wireless side and the user side according to feature relevance to obtain initialization model parameters.

[0078] Specifically, the feature extraction server selects a part of the wireless side data and user side data of the mobile network as the pre-training data set. The selected pre-training data set is preprocessed to initially form the feature support-subsidiary feature set of the data and the corresponding support feature set and the corresponding feature shrinkage variable set.

[0079] This step S201 can be implemented as follows: input the pre-training data set into the feature selector to perform feature correlation calculation; according to the feature correlation calculation result, each data feature of the pre-training data set is classified into the corresponding s...

Embodiment 2

[0087] Such as image 3 As shown, a pre-training data set pre-grouping method provided by an embodiment of the present invention includes:

[0088] S2011: Input the pre-training data set into the feature selector to perform feature correlation calculation.

[0089] Among them, suppose the number of initial groups γ of the feature selector, the maximum number of groups γ max , The associated parameter set {κ A , Κ B , Κ C }, γ and number γ max Is a natural number, and γ max , Κ A Represents the irrelevant threshold, κ B Represents the weak correlation threshold, κ C Represents the strong correlation threshold, and 0≤κ A B C ≤1.

[0090] Assuming that κ is set in advance A =0.1, κ B = 0.3, κ C =0.9, and the number of groups γ=10 and the maximum number of groups γ max =15, select a part of data from the wireless side data and user side data of the mobile network as the pre-training data set, and input the pre-training data set into the feature selector.

[0091] When the feature va...

Embodiment 3

[0112] Such as Figure 4 As shown, a training data set grouping method provided by an embodiment of the present invention includes:

[0113] S2021: Input the training data set and the initialization model parameters into the feature selector, and perform group feature correlation calculation on each data feature of the training data set.

[0114] Specifically, the newly-added wireless side and user side training data sets and initialization model parameters are input to the feature selector.

[0115] When the grouped feature value is linear data, the following formula is used to calculate the group feature association for each data feature of the training data set:

[0116]

[0117] Where f j Represents the data feature, j represents the data feature number in the training data set; Represents the label of the grouping feature, i represents the number of the group; Representation feature f j Group with Characteristic correlation coefficient of Representation feature f j Group with ...

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Abstract

The invention discloses a feature extraction method, device and server, and a computer readable memory medium and belongs to the field of the mobile Internet. The method comprises the steps of: pre-grouping a pre-training data set of a wireless side and a user side according to feature relevance, thereby obtaining initial model parameters; carrying out grouping feature relevance computing on the training data set according to the initial model parameters, classifying features of the training data set into corresponding groups, and updating the model parameters; and carrying out iterative optimization on feature contraction variables of each group of features, thereby obtaining a feature selection result of the wireless side and the user side. According to the method, the device, the serverand the computer readable memory medium, through utilization of a feature grouping selection mechanism, feature samples are pre-grouped before feature extraction is carried out; the grouping featurerelevance computing can be carried out on newly increased features; the feature redundancy computing problem can be effectively solved; grouping optimization is further carried out on grouping features through importing of the feature contraction variables; feature grouping selection efficiency is greatly improved; and online dynamic data feature extraction can be supported.

Description

Technical field [0001] The present invention relates to the technical field of mobile Internet, in particular to a feature extraction method, device, server and computer readable storage medium. Background technique [0002] In recent years, mobile Internet data traffic has exploded and the types of services are extremely rich. The behavior of different services has an increasingly profound impact on network performance. Therefore, research on user-side DPI (deep packet inspection, deep packet inspection) and wireless resource utilization on the wireless side The correlation between the rates is particularly important for further obtaining the 4G network expansion logic. [0003] At present, in order to realize the rational use of resources, it is necessary to further analyze the characteristics of the resources. In the feature extraction research, the method of alleviating the dimensional disaster by removing irrelevant and redundant features is to compare the characteristics of t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26H04L12/24
CPCH04L41/145H04L43/022H04L43/028
Inventor 邵敏峰
Owner ZTE CORP
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