Approximate Markov Blanket Feature Selection Method Based on Lasso Fusion

In feature selection, approximate Markov blankets are often used to judge redundant features, but the redundant features obtained are not identical. Therefore, when using approximate Markov blankets directly to delete redundant features, there may be situations that may lead to information loss and affect model accuracy. Therefore, an approximate Markov blanket feature selection method based on Lasso fusion for high-dimensional small sample data of traditional Chinese medicine metabonomics is proposed. The method is divided into two stages. In the first stage, irrelevant features are filtered by analyzing the correlation of features with the maximum information coefficient. In the second stage, approximate Markov blankets are used to construct similar feature groups, Lasso is used to evaluate the influence of features in similar feature groups, and redundant features are removed iteratively. The experimental results show that the algorithm can reduce the loss of useful information, remove irrelevant features and redundant features, and improve the accuracy and stability of the model. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.