Data classification model training method, data classification method and device

Abstract

The invention discloses a data classification model training method and device and a data classification method and device. The data classification model training method comprises the steps of obtaining a training data set; performing data processing on the training data set to determine a target data set; and constructing a data classification model, and training the data classification model by using the target data set to generate a target data classification model. According to the chronic gastritis diagnosis algorithm based on feature fusion and field negative sample elimination, diagnosis of chronic gastritis can be more objective, and repeatability is higher; by adopting the feature fusion and domain negative sample elimination algorithm, the problems of few available features, class imbalance and data overlapping of special data can be solved, and the method is more accurate than a traditional machine learning method.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a data classification model training method, a data classification method and a device. Background technique [0002] With the development of deep learning technology, people can effectively deal with machine classification problems such as data classification by using deep learning methods. When performing data classification, it is necessary to train the data classification model first, and perform data classification based on the data classification model. In order to improve the classification accuracy of the data classification model, it is often necessary to provide a large amount of sample data, and more sample data also means a heavy computational burden. However, for types of data such as chronic gastritis data, there are class imbalance and data overlap problems. The class imbalance problem will make the model too biased towards multi-class samples during the t...

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Problems solved by technology

[0003] In view of this, the embodiment of the present invention provides a data classification model training method, data classification method and device, which solves the problem of poor robustness and poor accuracy of the data classification model in the prior art

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