基于改进随机森林算法的评估指标精简方法研究

Research on the method of simplifying evaluation index based on improved random forest algorithm

为快速、准确获得武器装备系统效能评估结果,提出一种能够精简武器装备系统效能评估指标的方法。以随机森林(random forest,RF)算法度量的评估指标重要性为基础,通过改进RF算法中决策树划分属性选择、叶结点取值确定、决策树输出值计算方法,实现在指标值有缺失的情况下,对评估指标进行重要性的排序,进而精简出指定数量的重要评估指标。以防空系统效能评估指标精简为应用场景,基于指标值有缺失的模拟数据集,验证了提出的改进RF算法比传统RF算法具有更高的准确性。

To obtain the effectiveness evaluation results of weapon equipment system quickly and accurately,a method is proposed that can simplify the effectiveness evaluation index of weapon equipment systems.Based on the importance of evaluation index measured by random forest(RF)algorithm,the importance of evaluation index can be sorted in the case of missing index value,and then the specified number of important evaluation index can be refined by improving the decision tree partition attribute selection,leaf node value determination,and decision tree output value calculation methods in RF algorithm.Based on the simulation data set with missing index value,it is verified that the improved RF algorithm is more accurate than the traditional RF algorithm in the application scenario of air defense system effectiveness evaluation index simplification.