Classification model training and using method and device, equipment and medium

Abstract

The invention discloses a classification model training and using method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The training method comprises the steps of utilizing a training sample set, adopting an ensemble learning algorithm to train a classification model to obtain at least one classifier set, wherein the classifier set comprises at least two base classifiers; testing the classifier set by adopting a test sample set to determine the classification performance of each base classifier, and determining the classification weight of each base classifier according to the classification performance, wherein the classification weight is used for determining the weight of the classification result of each base classifier when the classifier set is used for sample classification. According to the embodiment of the invention, the diversity of the base classifiers in ensemble learning can be ensured, and the classifiers have the common classification performance for minority classes and majority classes, so that the classification model is suitable for the condition of unbalanced samples.

Description

technical field [0001] This application relates to the field of computer technology, in particular to an artificial intelligence technology. Background technique [0002] In recent years, with the rapid development of big data and artificial intelligence technology, speech and image recognition, natural language processing and knowledge graph have become hot research fields. Classification problems are the most typical problems in the field of machine learning and data mining. [0003] However, in real-world application scenarios, most classification data sets have obvious category distribution imbalance problems, that is, in the same data sample, the number of samples of some categories is much larger than that of other categories or a certain category. This kind of data set is called a category-imbalanced data set. An unbalanced data set is likely to lead to poor performance of the trained model, making it unable to achieve the desired application effect and application p...

AI Technical Summary

Problems solved by technology

[0003] However, in real-world application scenarios, classification data sets mostly have obvious category distribution imbalance problems, that is, in the same data sample, the number of samples of some categories is much larger than that of other categories or a certain category.

This kind of data set is called category imbalance data set, and the unbalanced data set is likely to lead to poor performance of the trained model, making it impossible to achieve the ideal application effect and application performance

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