Sample label determination method and device for financial data and electronic device

Abstract

The invention relates to a sample label determination method and device for financial data, an electronic device and a computer readable medium. The method comprises the following steps: determining multi-dimensional financial data characteristic values of a plurality of users in a positive sample set and an unclassified sample set; generating a target hyper-sphere equation through the multi-dimensional financial data feature values of the plurality of users in the positive sample set and a hyper-sphere equation; substituting the multi-dimensional financial data characteristic values of the users in the unclassified sample set into the target hypersphere equation to obtain the hypersphere distance of the users; and comparing the hyper-sphere distance of the user with a threshold value to determine a sample label of the user, the sample label including a positive sample label and a negative sample label. According to the sample label determination method for financial data, positive samples in unclassified samples can be extracted, and the positive samples and the negative samples are accurately classified, so that the calculation effect and the calculation precision of a machine learning model are improved.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, and in particular, to a method, device, electronic device, and computer-readable medium for determining a sample label of financial data. Background technique [0002] Machine learning has been greatly developed in various artificial intelligence research fields. Common machine learning models can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Each category can be divided into different types. algorithm. In most application scenarios today, people can easily find machine learning models suitable for their own problems. For the general application of machine learning models, the user first determines the machine learning model of a certain category or algorithm, and then according to the specific problem the user wants to solve, the user inputs specific data, the machine learning model establishes a specific task, a...

AI Technical Summary

Problems solved by technology

If the unlabeled samples are directly used as negative samples to train the binary classification model in the machine learning model, since there are a large number of positive samples in the unlabeled samples, a lot of error data will be introduced, which may lead to the unsatisfactory effect of the final model trained.

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