Classification model training method and device

Abstract

The present invention discloses a classification model training method and a device, belonging to the technical field of data processing. The method comprise a step of carrying out model training according to a current round of positive example samples and a current round of negative example samples and obtaining a current round of classification model, a step of using the current round of classification model to classify all samples if the current round of classification model does not satisfies a specified condition and selecting a specified sample in all samples according to a classification result, a step of taking the current round of positive example samples and the specified samples as a next round of positive example samples and determining a next round of negative example samples according to the next round of positive example samples, and a step of continuously executing the above model training and sample processing process according to the next round of positive example samples and the next round of negative example samples until the classification model which satisfies the specified condition is obtained. With the increase of the positive example sample number, the potential positive example samples in the negative example samples decrease, the purity of the negative example samples can be effectively improved, the stability of model obtained through training according to superimposed number of positive example samples and negative example samples, and the classification accuracy is high.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a classification model training method and device. Background technique [0002] With the continuous development of information technology, we have entered the era of big data. For example, merchants or enterprises can collect massive user data through various service platforms they provide. Among them, a variety of useful information is often hidden in massive data, which can usually provide great help to business management, production control, market analysis, engineering design or scientific exploration, etc., so data mining technology is widely used by people in various fields. of great concern. Among them, the basic task of data mining is to classify massive data, and data classification is usually based on a trained classification model. [0003] In the current technology, when training the classification model, first select positive samples and negative samples...

AI Technical Summary

Problems solved by technology

[0005] When the total number of samples is small, the number of positive samples and negative samples will decrease accordingly, and some positive samples may be included in the negative samples, resulting in low purity of negative samples. Therefore, the classification model obtained after a round of model training based on such positive samples and negative samples is not stable, the classification accuracy is low, and even underfitting occurs.

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