Model label labeling method and device based on support vector machine and medium

A support vector machine and model technology, which is applied in kernel methods, computer components, character and pattern recognition, etc., can solve the problems of inability to obtain labeled data, insufficient training samples for credit models, and inability to train credit scoring models. Avoid subjective influence, save labor cost, manual labeling time, and the effect of accurate labeling results

Pending Publication Date: 2022-01-21
天元大数据信用管理有限公司
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[0004] One or more embodiments of this specification provide a model labeling method, device, and medium based on support vector machines, which are used to solve the following technical problems: In actual appl

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  • Model label labeling method and device based on support vector machine and medium
  • Model label labeling method and device based on support vector machine and medium
  • Model label labeling method and device based on support vector machine and medium

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[0025] In order to better understand the technical solution in the present specification, it will be described in conjunction with the drawings in the present specification, and will be described in conjunction with the drawings in the present specification. The embodiment is merely a part of the embodiments of the present specification, not all of the embodiments. Based on the present specification, one of ordinary skill in the art should belong to the scope of this specification without all other embodiments obtained by creative labor.

[0026] With the rapid development of Internet technology, people's daily work and living habits have changed. The development and application of Internet technology penetrates all walks of life, especially financial industries. The traditional financial industry is accepting big data, the Internet, and user experience unbalanced asymmetry. Big data wind control technology has become a big data application focus on industry insiders. The financia...

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Abstract

The embodiment of the invention discloses a model label labeling method and device based on a support vector machine and a medium, and the method comprises the steps of building an index system of a credit model according to the credit data of an enterprise, and determining the index data; classifying preset index data in the index data, and labeling labels; according to the labeled preset index data, training a pre-constructed sample label labeling model to obtain a sample label labeling model meeting requirements, and determining a corresponding relationship between index features of the preset index data and labeled labels, the sample label labeling model being a support vector machine model; analyzing the index characteristics of the unlabeled index data in the index data through the sample label labeling model meeting the requirements, and classifying and labeling the unlabeled index data according to the corresponding relationship between the index characteristics of the preset index data and the labeled labels, so as to train a credit model according to the labeled index data, and obtaining a credit model meeting the requirements.

Description

technical field [0001] This specification relates to the field of computer technology, in particular to a model labeling method, device and medium based on a support vector machine. Background technique [0002] The development and application of Internet technology has penetrated into various industries, especially the financial industry. The traditional financial industry is facing the severe test of big data, the Internet, and the imbalance and asymmetry of user experience. The use of big data risk control technology has become a big data application focus that the industry pays the most attention to. [0003] In the current Internet financial credit system, the credit scoring model is usually used to evaluate the credit of enterprises. The common method of credit scoring model is logistic regression. When using logistic regression to build a credit scoring model, a large number of labels containing However, in the actual application environment, in many cases, a large a...

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Application Information

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IPC IPC(8): G06Q10/06G06Q40/02G06F16/25G06K9/62G06N20/10
CPCG06Q10/06393G06Q10/0635G06N20/10G06F16/258G06Q40/03G06F18/2411G06F18/25
Inventor 郭长营崔乐乐
Owner 天元大数据信用管理有限公司
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