Classification model training method and device and data classification method and device

Abstract

The invention provides a classification model training method and device and a data classification method and device. The classification model training method comprises: acquiring a sample data set, the sample data set comprising at least three category labels and feature data corresponding to the category labels, and counting the proportion of the number of each category label in the sample dataset; according to the proportion of the number of each category label in the sample data set, dividing the category labels in the sample data set into at least two sample groups; and inputting the sample group into a corresponding classification model for training until a training condition is met. According to the method, the classification labels in the sample data set are unbalanced in proportion, the quality of the processed sample data set is greatly improved, the training effect of the classification model can be further ensured, and the classification accuracy of the trained classification model is greatly improved during actual classification prediction of the trained classification model.

Description

technical field [0001] The present application relates to the technical field of data classification, and in particular to a classification model training method and device, a data classification method and device, computing equipment, and a computer-readable storage medium. Background technique [0002] Data classification is to automatically classify and mark data according to a certain classification system or standard, such as text classification, to automatically classify the input text according to a certain category system. Text classification technology has been widely used in natural language processing fields such as text review, advertisement filtering, sentiment analysis and anti-pornography recognition. [0003] In the training method of the existing classification model, the sample data is generally directly selected from the sample data set and input into the classification model to train the classification model, but the number of samples of one category in t...

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Problems solved by technology

[0003] In the training method of the existing classification model, the sample data is generally directly selected from the sample data set and input into the classification model to train the classification model, but the number of samples of one category in the sample data set is much larger than the number of samples of other categories In some cases, the category imbalance of the number of samples in the sample set used for training, that is, the category imbalance of the number of samples in the sample data set, the trained classification model is only good for the classification of samples of different categories in the training sample set, but When classifying the original data set, due to the large difference in the proportion of categories between the original data set and the training sample set, the error rate of the classification result predicted by the trained classification model is high, and the existing trained classification model is difficult to carry out practical application

[0004] In the training of classification models, if you want to establish a training set with balanced sample categories, you need to spend a lot of manpower and material resources to find and process materials to obtain a training set with balanced sample categories, which will greatly increase the cost of classification model training.

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