改进的KNN算法在光测图像关键事件评估中的应用

The Application of Improved KNN Algorithm in Optical Image Key Events Assessment

KNN算法是光测图像关键事件评估中常用的算法,经典的KNN算法只注重候选范例的个数,而忽视候选范例个体的特殊性,因此KNN方法在某些时候会使得评估结论极不合理。基于此,本文提出了改进的KNN算法,该算法更加注重候选范例的个体性,候选范例到目标范例的距离、候选范例的概率分布等,对目标范例的评估结论都有重要影响。实验结果表明,本文提出的KNN改进算法比经典KNN算法评估结论更准确,计算出的隶属度表征了关键事件成功失败的程度,结论更实际更合理。

KNN algorithm is a commonly used algorithm in the assessment of optical image key event. However, classical KNN algorithm always makes conclusion unreasonable, because it only concerns about the number of candidate cases, neglecting of candidate cases’ private characters. To solve this problem, an improved KNN algorithm was proposed, which focused on the private characters of candidate cases. This paper argued that, the distance between candidate cases and target case, and the probability distribution of the candidate cases, both had important influence on last conclusion of target case. The test results showed that, the KNN algorithm proposed was more accurate than classical KNN algorithm, and the membership in proposed KNN algorithm represented degree of success or failure, which were more practical and more reasonable in the engineering practice.