A method for completing and predicting an attribute missing data set based on generative adversarial network

Abstract

The invention discloses a method for completing and predicting an attribute missing data set based on a generative adversarial network. The method comprises the following steps: 1) normalizing data minmax and using one hot code for discrete attribute, wherein the missing value is marked as 0; 2) using that data set to establish a missing position coding vector with respect to the sample; 3) constructing a generative antagonistic network and an auxiliary prediction network for data filling and label prediction; 4) restoring that result before minmax normalization according to the maximum and minimum value in the attribute; 5) selecting a suitable super parameter through testing. The data distribution information and the label information in the data set are fully utilized, and at the same time, another auxiliary prediction network included in the method can directly input attribute missing data to give the prediction result of the label after the training is completed. The process is simple and has higher prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of data preprocessing, in particular to a method for complementing and predicting missing attribute datasets based on generative confrontation networks. Background technique [0002] The phenomenon of missing attributes of data sets exists widely in various data sets, and is usually caused by information loss during data collection or transmission. The loss of one or more attributes of the samples in the data set will reduce the prediction accuracy of the subsequent prediction and classification models. How to complete these missing data and use the information contained in samples with missing attributes to build a high-precision prediction model is a key problem in data preprocessing. [0003] Most statistical tools deal with the problem of missing attributes by deleting the rows and columns corresponding to the missing samples, or using the median and average of the column to fill in the missing ...

AI Technical Summary

Problems solved by technology

These algorithms often achieve more accurate filling results than the mean and median when the amount of data is sufficient, and there are usually some problems: in the regression filling method, there is a significant linear relationship between attributes, and based on EM The filling method of the algorithm has high computational complexity and is easy to fall into a local optimum; the filling method based on k-nearest neighbors is simple to implement, but when faced with a large amount of data, the large amount of calculation and extremely high complexity lead to difficult calculation

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