Bayesian network model structure based on binary evolutionary algorithm.

With the continuous development of new technologies, the scale of training data is also expanding. Machine learning algorithms are gradually beginning to be studied and applied in places where the scale of data is relatively large. Because the current structure of learning algorithms only focus on the identification of dependencies and ignores the direction of dependencies, it causes multiple labeled samples not to identify categories. Multiple labels need to be classified using techniques such as machine learning and then applied to solve the problem. In the environment of more training data, it is very meaningful to explore the structure extension to identify the dependencies between attributes and take into account the direction of dependencies. In this article, Bayesian network structure learning, analysis of the shortcomings of traditional algorithms, and binary evolutionary algorithm are applied to the randomized algorithm to generate the initial population. In the optimization process of the algorithm, it uses a Bayesian network to do a local search and uses a depth-first algorithm to break the loop. Finally, it finds a higher score for the network structure. In the simulation experiment, the classic data sets, ALARM and INSURANCE, are introduced to verify the effectiveness of the algorithm. Compared with NOTEARS and the Expectation-Maximization (EM) algorithm, the weight evaluation index of this article was 4.5% and 7.3% better than other schemes. The clustering effect was improved by 13.5% and 15.2%. The smallest error and the highest accuracy are also better than other schemes. The discussion of Bayesian reasoning in this article has very important theoretical and practical significance. This article further improves the Bayesian network structure and optimizes the performance of the classifier, which plays a very important role in promoting the expansion of the network structure and provides innovative thinking.