Research on Feature Selection Method Based on Bayesian Network and Importance Measures

With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.