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Method and device for optimizing a risk identification model

A risk identification and model technology, applied in the field of machine learning, can solve problems such as the limited number of wrongly identified objects, optimize risk identification models, etc., achieve accurate acquisition, improve identification accuracy, and get rid of artificial dependence

Active Publication Date: 2022-04-22
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] However, the number of misidentified objects found is often limited, and risk identification models cannot be well optimized based on these small number of objects only.

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  • Method and device for optimizing a risk identification model
  • Method and device for optimizing a risk identification model
  • Method and device for optimizing a risk identification model

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Embodiment Construction

[0025] In practical applications, risk identification can be performed by training a risk identification model. However, when the risk identification model is actually applied to identify whether the object to be identified is at risk, there may be misidentification.

[0026] For example, when the business party dealing with the actual business uses the risk identification model to identify the risk of the object to be identified, the identification result obtained is risky, but after manual inspection by the business party, it is found that the object to be identified is actually risk-free; or, it is obtained The identification result is risk-free, but the business side finds that the object to be identified actually has a risk through manual inspection.

[0027] At this time, the business side needs to feed back the object to be identified that was incorrectly identified by the risk identification model, that is, the misidentified object. After receiving the misidentified o...

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Abstract

This specification discloses an optimization method and device for a risk identification model. The method includes: obtaining misidentified samples and N candidate samples; inputting the feature vectors of the misidentified samples and each candidate sample into a deep learning model for processing, and extracting the processed eigenvector, as the normalized eigenvector of the corresponding sample; according to the normalized eigenvector of the misrecognized sample and the normalized eigenvectors of the M candidate samples, the K nearest neighbor algorithm is used to determine from the M candidate samples the K candidate samples similar to the misidentified samples are used as similar samples; the label of each similar sample is determined as the first risk type, and the risk identification model is optimized based on the misidentified samples and each similar sample.

Description

technical field [0001] The embodiments of this specification relate to the field of machine learning, and in particular to a method and device for optimizing a risk identification model. Background technique [0002] When the risk identification model is actually used, it is possible to find objects that are misidentified by the risk identification model. For example, the risk identification model identifies an actual risky object to be identified as risk-free, or identifies an actual risk-free object to be identified as risky. In this case, it is generally necessary to optimize the risk identification model by taking the found misidentified objects as samples, so as to improve the identification accuracy of the risk identification model. [0003] However, the number of misidentified objects found is often limited, and risk identification models cannot be well optimized based only on these small number of objects. Contents of the invention [0004] In order to further im...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N20/00
CPCG06Q10/04G06Q10/0635G06N20/00
Inventor 叶芸
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD