Brain network data enhancement method based on forest auto-encoder

Abstract

The invention relates to a brain network data enhancement method based on a forest auto-encoder, and belongs to the field of machine learning data enhancement method theory and application research. The method specifically comprises the following steps of generating original data, generating sparse brain network data and screening the generated sparse brain network data. The sparse brain network data is generated by utilizing the characteristic that the generator based on the forest auto-encoder can influence the sparse degree of decoded data by adjusting the number of trees, and the generateddata is screened by utilizing the screener based on a plurality of random forests, so that the sparsity and robustness of the generated data are ensured. The screened data is used for data enhancement, so that the classification performance of various classifiers is improved. The method has few parameters, the generated data is more consistent with the essential characteristics of the brain network, and the method has a very wide application prospect.

Description

technical field [0001] The invention belongs to the theory and applied research field of machine learning data enhancement method, and specifically relates to a brain network data enhancement method based on a forest autoencoder. Background technique [0002] The human brain is an extremely complex information processing system that can perform complex tasks through the interconnection of multiple neurons, groups of neurons, and brain regions. At the same time, human cognitive processes depend on the interaction of different regions of the brain, and the patterns of these interactions are called functional brain networks. In recent years, more and more studies have shown that many neurological and psychiatric diseases, including Alzheimer's disease (AD), schizophrenia, and autism spectrum disorder (ASD) Usually accompanied by disruption or abnormal integration of connections between brain regions. Therefore, the study of brain networks provides an opportunity to explore th...

AI Technical Summary

Problems solved by technology

However, the functional connection of the brain network should be sparse in nature, and our commonly used brain network data is composed of correlation coefficients between every two brain regions, and there are a lot of noise and unnecessary connections.

However, current data generation methods are difficult to remove these unnecessary connections and noises while obeying the original data distribution

Images