Model training method and device based on risk identification and electronic equipment

A technology for model training and risk identification, applied in the computer field to improve the accuracy of the model

Active Publication Date: 2020-11-13
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although for the convenience of operation, unlabeled samples can be used as negative samples for training, bu

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  • Model training method and device based on risk identification and electronic equipment
  • Model training method and device based on risk identification and electronic equipment
  • Model training method and device based on risk identification and electronic equipment

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[0067] For purposes of this application, technical solutions and advantages clearer, the present specification in conjunction with the following specific examples and corresponding figures of the present application aspect clearly and completely described. Obviously, the described embodiments are merely part of embodiments of the present description, but not all embodiments. Based on the embodiments in this specification, all other embodiments to those of ordinary skill in the art without any creative effort shall fall within the scope of the present application.

[0068] In conjunction with the following drawings, detailed description of the technical solutions provided in each embodiment of the present specification.

[0069] figure 1 Example flowchart of one kind proposed risk identification model training method embodiments of the present specification. The present specification, the embodiment can be used to train the model identify areas of risk, for example, the risk of th...

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Abstract

The embodiment of the invention discloses a model training method and device based on risk identification and electronic equipment. According to the specific scheme, the method comprises the steps ofobtaining a first data set without a sample label, wherein the first data set comprises sample data expected to have a first type of sample label, and the sample data expected to have the first type of sample label is doped with sample data with a second type of sample label; and pre-configuring a first type of sample tags for the first data set, and operating the target model configured with thefirst model parameters by utilizing the first data set to generate a prediction value; utilizing a loss function to judge the loss amount of the predicted value relative to a target value reflected bythe first data set; and estimating a statistical center estimated value of the first data set corresponding to the loss amount, converting the statistical center estimated value into a statistical center expected value, and adjusting the first model parameter by using the loss amount and the statistical center expected value corresponding to the loss amount until the loss amount reaches a presetcondition.

Description

technical field [0001] The embodiments of this specification relate to the field of computer technology, and in particular to a risk identification-based model training method, device and electronic equipment. Background technique [0002] Machine learning is a sub-discipline of artificial intelligence. Its main research is to let machines learn from past experiences, model the uncertainty of data, and predict the future. Generally, machine learning methods include the following two categories: [0003] a. Supervised methods: When there is labeled data, supervised methods can usually achieve stronger generalization capabilities; [0004] b. Unsupervised method: No data labeling is required, and malicious attacks can be prevented through anomaly detection technology; [0005] In actual business scenarios, in more cases, only a small number of positive samples and a large number of unlabeled samples may be obtained, but there may still be a small number of positive samples i...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06K9/62
CPCG06Q10/04G06Q10/0635G06F18/214
Inventor 吕乐傅幸周璟宝鹏庆王维强
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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