Abstract:

According to the problem of the endless flowing of data stream of Combat simulation and the affections of concept phenomenon on its classification model accuracy, an incremental learning simple Bayesian classification algorithm based on Weight window is proposed. The algorithm build a weight window on the data stream of equipment combat simulation in order to learn a new classification model with taking full advantage of the historical time data. The goal is to reduce the unlimited inflows of data stream of equipment combat simulation and mitigate the impact of the concept drift on the accuracy of the classification model, thus improving the accuracy of the simple Bayesian classifier model. Numerical experiments also prove that the algorithm is effective and outperforms other similar algorithm counterparts in terms of classification performance, classification accuracy rate and classification.        

Key words: equipment combat simulation;data stream classification;concept drift;Bayesian