基于代价敏感集成学习的战场态势评估模型

Battlefield Situation Assessment Model Based on Cost-sensitive Ensemble Learning

针对误分代价不平衡条件下基于机器学习的战场态势评估误分代价较高问题,提出基于代价敏感集成学习的战场态势评估模型。以具有良好非线性建模能力的BP神经网络模型为基础分类器,通过AdaCost代价敏感集成学习方法综合考虑误分类代价对BP神经网络进行集成训练,使模型具有代价敏感特性。实验表明,在误分代价不平衡条件下,相对于单独的BP神经网络模型来说,所提模型在评估准确率和误分总代价方面都有较大优势。

Aiming at the problem that the misclassification cost of battlefield situation assessment based on machine learning is high under the condition of unbalanced misclassification cost,a battlefield situation assessment model based on cost-sensitive en⁃semble learning is proposed.Based on the BP neural network model with good nonlinear modeling ability,the cost-sensitive ensem⁃ble learning method of AdaCost is used to train the BP neural network to make the model cost sensitive.The experimental results show that the proposed model has great advantages in the evaluation accuracy and the total cost of misclassification compared with the single BP neural network model under the condition of unbalanced misclassification cost.