Missing feature re-representation method and system based on graph convolutional network

Abstract

The invention provides a missing feature re-representation method and system based on a graph convolutional network. The method comprises the steps: extracting the features of a training sample with a marked category, taking the features of the training sample as nodes, obtaining the similarity relation between the nodes through distance measurement, constructing a connection edge between the nodes according to the similarity relation, and carrying out the re-representation of the missing features of the training sample. Obtaining a graph network of the training sample; Training a graph convolutional network according to the feature information of the adjacent samples in the graph network and the annotation categories corresponding to the nodes in the graph to obtain a feature re-representation model, inputting the features of the to-be-classified samples into the feature re-representation model, reconstructing the features of the to-be-classified samples, and classifying the reconstructed features to obtain a classification result. And obtaining a classification result of the to-be-classified sample.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a method for re-expressing missing features based on graph convolutional networks. As a method for dealing with abnormal data such as missing features in machine learning, this method can effectively solve the problem of model failure caused by missing features. Background technique [0002] The current machine learning model method needs to keep the feature dimension consistent, but in the real environment, there is a problem of missing source data, such as unstable wireless signals, the characteristics of the sensor itself, and severe changes in the highly dynamic environment will cause data loss situation, and the severity increases with the continuous expansion of the range of environments to which the model is adapted. The lack of data will lead to problems such as increased difficulty in extracting features, thereby reducing the performance of machine learning models. [0...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the difficulty of model training due to the lack of features in the process of machine learning. On this issue, a method for re-representing data missing features based on graph convolutional networks is proposed.

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