Research on Optimization of Markov Blanket Algorithm Based on Bayesian Network

With the advent of the big data era, the scale and complexity of data are con-stantly increasing, and feature selection has become one of the key issues in improving the performance and computational efficiency of machine learning models.15 The Markov carpet algorithm is widely used in feature selection and data dimensionality reduction tasks because it can effectively describe the con-ditional dependencies among variables in Bayesian networks. This paper pro-poses an optimization method for the computational efficiency and accura-cy problems of the existing IPC-MB and IAMB algorithms in high-dimensional data processing. The optimization methods include the optimization of sorting strat-egies, the improvement of filtering strategies, and the application of sym-metry principles. Through theoretical analysis and experimental verification, this pa-per proves the performance improvement of the optimized algorithm on multi-ple datasets. The optimized IPC-MB and IAMB algorithms are significantly supe-rior to the original algorithms in terms of computing time and accuracy. The experimental results show that the optimized algorithm can efficiently pro-cess high-dimensional data and has strong stability and application prospects.