Regression modeling method based on regression attention generative adversarial network data enhancement

Abstract

The invention discloses a regression modeling method based on regression attention generative adversarial network data enhancement. A regression attention generative adversarial network adds attentionmechanisms to a generator and a discriminator; an attention module 1 in the generator constructs regression loss by utilizing independent variables and dependent variables of generated data output bythe generator; meanwhile, the attention module 1 is finely adjusted through real data; an attention module 2 in the discriminator constructs a new loss by using a difference value between the real data and the generated data regression loss; according to the invention, the feature containing the maximum regression information is extracted by minimizing the loss, and the feature contains the regression difference information between the maximized real data and the generated data, so that consideration of a discriminator on the regression information is facilitated. Based on the regression attention generative adversarial network, the original data is enhanced by utilizing the generated data, and then regression modeling is carried out by utilizing a data driving method, so that the performance and the prediction precision of the regression model are effectively improved.

Description

technical field [0001] The invention belongs to the field of industrial process soft measurement, in particular to a regression modeling method based on regression attention generation against network data enhancement. Background technique [0002] In today's big data era, data-driven models play an important role. Among them, the regression model is widely used in many scenarios as a practical tool, such as stock trend prediction in the financial industry, soft sensors in the process industry, etc. The quality of data is critical for data-driven models. In application scenarios such as limited data accumulation, difficult data acquisition, and data privacy protection, the lack of data affects the prediction accuracy of the regression model. Therefore, how to improve the performance of regression models under limited data is an important topic. [0003] Generative confrontation network (GAN) is a generative model proposed by Goodfellow in 2014. Adding the generated data ...

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Problems solved by technology

Therefore, these factors limit the effectiveness of data-augmented regression modeling

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