System resource allocation method and device, and equipment

Abstract

The embodiment of the invention relates to the technical field of artificial intelligence, and discloses a system resource allocation method and device, and equipment, and the method comprises the steps: obtaining a specified information set with feature data used for representing user risk features, and a tag set; the specified information set at least comprises a prediction sample set and three reference sample sets; the label set comprises risk categories corresponding to the reference samples; correspondingly constructing at least three sub-classifiers by utilizing at least three reference sample sets and the label set; performing risk classification on the prediction sample by using each sub-classifier to obtain a risk classification result of the prediction sample relative to each sub-classifier and a misclassification cost of the prediction sample; and optimizing each sub-classifier according to the prediction sample set, the risk classification result of the prediction sample and the misclassification cost of the prediction sample to obtain each optimized sub-classifier, and allocating system resources to the target user based on the risk prediction result of each optimized sub-classifier for the target user.

Description

technical field [0001] This specification relates to the technical field of artificial intelligence, and in particular, relates to a system resource allocation method, device and equipment. Background technique [0002] With the rapid development of big data service platform technology, financial resource service types and alternative service channels are becoming more and more diverse and convenient, and user risk prediction is becoming more and more important for financial institutions. For example, for some online loan businesses with relatively convenient service channels, due to relatively little manual intervention, if user risk prediction is not accurate enough, there may be problems such as unreasonable resource allocation and poor user experience. [0003] At present, the commonly used user risk prediction methods are mainly classification methods based on supervised learning models. Through modeling based on known customer risk data, the trained model is used to pr...

AI Technical Summary

Problems solved by technology

However, there are usually fewer samples of known user risks, and the sample data of known user risks may not be able to accurately characterize the risk characteristics of users under the new service type, thus affecting the accuracy of user risk prediction and affecting the allocation to corresponding users. The accuracy of user resources may lead to unreasonable resource allocation and reduce the user experience

[0004] For the above problems, no effective solution has been proposed

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