User identification method and device based on adversarial migration and electronic equipment

A user identification and user data technology, which is applied in the field of electronic equipment and computer readable media, devices, and user identification methods based on anti-migration, can solve problems such as poor risk control model effect, impact on risk control effect, and inaccurate label classification. , to achieve the effect of improving classification accuracy and improving risk control effect

Inactive Publication Date: 2020-09-04
北京淇瑀信息科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention aims to solve the technical problem that the label classification of unlabeled samples in the user data in the existing big data processing technology is inaccurate, which leads to poor effect of the risk control model and affects the final risk control effect

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  • User identification method and device based on adversarial migration and electronic equipment
  • User identification method and device based on adversarial migration and electronic equipment
  • User identification method and device based on adversarial migration and electronic equipment

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Embodiment Construction

[0049] Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings, and although the exemplary embodiments may be embodied in many specific forms, these should not be construed as limited to the embodiments set forth herein. On the contrary, these exemplary embodiments are provided in order to make the content of the present invention more complete and more convenient to fully convey the inventive concept to those skilled in the art.

[0050] On the premise of conforming to the technical concept of the present invention, the structure, performance, effect or other features described in a specific embodiment can be combined into one or more other embodiments in any suitable manner.

[0051] During the introduction of specific embodiments, detailed descriptions of structures, performances, effects or other features are intended to enable those skilled in the art to fully understand the embodiments. However, it does no...

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Abstract

The invention discloses a user identification method and device based on adversarial migration and electronic equipment. The method comprises the following steps: acquiring source domain user data andtarget domain user data; wherein the source domain user data comprises a user attribute label; training a domain adversarial migration neural network model according to the source domain user data and the target domain user data; and identifying a user attribute label of the target domain user data through the domain adversarial migration neural network model. By introducing the confrontation layer, features capable of being migrated among different domains are selected and extracted. A label predictor with good performance is trained in a source domain, a source domain and a target domain are distinguished through a domain classifier in the training process, parameters of the label predictor and the domain discriminator are optimized according to a target function, and a classifier withgood performance in the target domain is obtained.

Description

technical field [0001] The present invention relates to the technical field of computer information processing, in particular to a user identification method, device, electronic equipment and computer-readable medium based on anti-migration. Background technique [0002] In the risk control system, it is usually necessary to train the risk control model through labeled sample data, and then use the trained risk control model to predict potential financial risks. In reality, only a small number of businesses (such as the financial resource allocation business in Internet finance) will accumulate a large number of labeled samples. For unlabeled or less labeled samples (such as H5 traffic samples), usually labeled samples and unlabeled or less labeled samples are mixed as training samples, and in this way, the amount of unlabeled sample data Far larger than the amount of labeled sample data, it will lead to inaccurate label classification and affect the final risk control effe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06N3/04G06Q40/00
CPCG06N3/08G06Q40/00G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 张国光宋孟楠苏绥绥
Owner 北京淇瑀信息科技有限公司
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