Multi-dimensional user feature vector screening method

A technology of user characteristics and screening methods, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of feature vector redundancy, high calculation cost, and inability to complete calculations, so as to avoid information redundancy and reduce time cost, the effect of good performance

Inactive Publication Date: 2019-09-27
RENMIN UNIVERSITY OF CHINA
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AI Technical Summary

Problems solved by technology

[0004] However, the existing multi-dimensional user feature vector screening methods have the following problems: 1) The calculation is mainly concentrated on all multi-dimensional users. When the number of users is huge, the calculation cost is high, and the calculation cannot even be completed
1) It is necessary to calculate the feature screening results on the selected part or the whole subset, and the feature vector redundancy is prone to appear in the integration process of the sub-feature screening results

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Embodiment Construction

[0020] The present invention will be described in detail below in conjunction with the accompanying drawings. However, it should be understood that the accompanying drawings are provided only for better understanding of the present invention, and they should not be construed as limiting the present invention.

[0021] Such as figure 1 As shown, one of the core concepts of the classic fuzzy rough set algorithm in the prior art is to calculate the dependence degree of each feature vector, and use the difference of the dependence degree before and after removing the feature vector as the importance measure for judging the feature vector, thus, Without assuming the distribution of user data, the importance of each feature vector can be calculated according to the concept of dependence of fuzzy rough sets, and the importance of the feature vector can be used as a criterion for screening multidimensional user feature vectors. The main steps include:

[0022] 1) Initialize algorithm...

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Abstract

The invention relates to a multi-dimensional user feature vector screening method, which is characterized by comprising the following steps of 1) extracting a certain feature vector quantum set in a multi-dimensional user feature vector; 2) calculating the dependency degree of each feature vector in the feature vector quantum set on the user class label; 3) adding the feature vector with the maximum dependency degree in the feature vector quantum set into a candidate feature vector quantum set, and determining the information increment of the candidate feature vector quantum set; 4) judging whether the information increment of the candidate feature vector quantum set meets the requirement or not by adopting an integrated learning method and according to a preset judgment condition, and if the information increment does not meet the requirement, entering the step 2) to re-extract the feature vector quantum set in the multi-dimensional user feature vector; and if the information increment meets the requirement, taking the candidate feature vector quantum set as an optimal feature vector quantum set to complete the screening of the multi-dimensional user feature vectors. The method can be widely applied to the field of large-scale multi-dimensional user data pre-processing.

Description

technical field [0001] The invention relates to a method for screening multi-dimensional user feature vectors, and belongs to the field of pre-processing of large-scale multi-dimensional user data. Background technique [0002] In recent years, with the continuous development of science and technology, human daily life has become more abundant, the dimension of user data has become higher and higher, and the scale of users has become larger and larger. Therefore, in the face of large-scale multi-dimensional user data, it is increasingly urgent to propose an efficient feature vector screening method. At present, the common eigenvector screening methods include filtering, wrapping and embedding. The filtering method first screens the user's feature vectors, and then trains the learner. The result of the user's feature vector screening has nothing to do with the subsequent learner. The wrapping method uses the performance of the learner to be used as the evaluation criterion ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/24
Inventor 赵素云王振磊秦波
Owner RENMIN UNIVERSITY OF CHINA
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