Air-Attack Weapon Identification Model of Weighted Navie Bayes Based on SOA
Chen Xi-cheng, Bing-bing Fan, Wen-jia Yang, Gui-long Tian, Kai Wu · Journal of Physics Conference Series · 2018
Traditional Navie Bayes algorithm exists the issues of low inefficiency for the Air-attack Weapon Identification. Inorder to solve this problem, Air-attack Weapon Identification model of Weighted Navie Bayes Based on Seeker Optimization Algorithm is proposed. Firstly, the model reduces the dimension of the data sam-ples using rough set theory. Secondly, Seeker Optimization Algorithm searches the best attribute weights of Weighted Naïve Bayes. Finally, Navie Bayes classifier is structured with the best attribute weights to complete detection. The combination of the two algorithms can not only solve the feature redundancy problem of the traditional Navie Bayes algorithm, but also can optimize the strong independence between features. Through the experiments, prove that using this model for Air-attack Weapon Identification in air defense combat identification