Data classification method and device, equipment and computer readable storage medium

Abstract

The invention provides a data classification method and device, equipment and a computer readable storage medium. According to the data classification method and device, the equipment and the computerreadable storage medium provided by the invention, all minority sample sets are mixed to form a new sample set which serves as training samples to perform machine learning together with majority sample sets so as to obtain a first classification model, so that a problem of imbalance in sample quantity of the minority sample sets and the majority sample sets is solved; and all of the minority sample sets serve as new samples to perform training again so as to obtain a second classification model, the type of the data is predicted through combining the first classification model and the secondclassification model, and a problem of inaccurate prediction for the minority data is solved.

Description

Technical field [0001] The present invention relates to the field of information processing technology, in particular to a data classification method, device, equipment and computer-readable storage medium. Background technique [0002] At present, in the process of data modeling and classification of data, especially in the case of multi-classification, there are often problems of imbalance in various types of samples. When the number of training samples of various types differs greatly, unbalanced samples are directly used If the classification model is obtained by training, the result of model training may be very unsatisfactory due to the imbalance of the number of various samples. Then the prediction result obtained by using the trained model to predict is not ideal, or even the opposite. [0003] The current common practice is to increase the number of samples with a smaller number of samples by generating new samples to achieve a level that is balanced with the number of sam...

AI Technical Summary

Problems solved by technology

[0002] At present, in the process of data modeling and classification of data, especially in the case of multi-classification, there are often problems of class imbalance in various samples. When the number of training samples of various types is quite different, the unbalanced samples are directly used. If the classification model is obtained by training, the result of model training may be very unsatisfactory due to the imbalance of the number of samples of various types. Then the prediction result obtained by using the trained model for prediction is not ideal, or even the prediction result is opposite.

[0003] At present, the more common practice is to increase the number of samples by generating new samples for those samples with a small number, so as to reach the level of equilibrium with the number of samples with a large number, but because the new samples are not real samples, and the generated There should not be too many new samples, so most of the more samples need to be discarded, but because most of the samples are discarded, the prediction results of the established data model may be inaccurate

Images