Classification model training method and device and electronic equipment
A classification model and category technology, applied in the field of artificial intelligence, can solve the problem of low generalization performance of classification models, and achieve the effect of improving prediction accuracy and generalization.
Pending Publication Date: 2021-10-29
INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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[0005] The purpose of the embodiment of the present application is to provide a training method, device and electronic equipment for a classification model, so as to solve the problem that the generalization performance of the existing classification model is not high in the face of unbalanced data classification
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[0017] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described The implementations are only some of the implementations of the present application, not all of them. Based on the implementation manners in this application, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.
[0018] In some scenario examples, there may be management and control transaction records among the massive financial transaction records, and the field values of such transaction records are often different from the field values of normal transaction records. In order to find the transaction records of the cont...
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The invention discloses a classification model training method and device and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining an original sample set; cyclically executing the following iterative operations until a preset termination condition is met: oversampling the original sample set and the reference sample set to obtain a training sample set; performing the following operations on each sample pair in the training sample set: inputting the feature data of the current sample pair into the classification model to obtain a prediction result; calculating the confidence coefficient of the classification model according to the category mark of the current sample pair and the probability that the current sample pair is divided into various categories; under the condition that the confidence is within a preset range, updating parameters of the classification model through the current sample pair; and putting the current sample pair into a reference sample set as a reference sample pair under the condition that the confidence is within a predetermined range and the category mark of the current sample pair belongs to a predetermined category. According to the scheme, the prediction accuracy and generalization of the classification model for small-class samples can be improved.
Description
technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a training method, device and electronic equipment for a classification model. Background technique [0002] The classification of unbalanced datasets has become one of the key and difficult issues in the field of intelligent machine learning in recent years. The so-called unbalanced data set refers to the fact that the sample size belonging to each category in the data set is extremely unbalanced. Taking the binary classification problem as an example, assuming that the number of samples of the positive class is much larger than that of the negative class, the data set in which the ratio of samples of different classes is close to 100:1 is usually called an unbalanced data set. These categories with a small sample size are also called small categories or small sample categories. The samples of small categories are often the focus of the classification...
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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/2411G06F18/214
Inventor 崔希庆姜俊萍郭邦
Owner INDUSTRIAL AND COMMERCIAL BANK OF CHINA