核化空间深度包围核的模糊决策异常检测算法

Fuzzy Decision Outlier Detection Algorithm of Kernelized Spatial Depth Sphere

根据核化空间深度异常检测算法中适用性的局限性和最小包围核算法中存在参数影响检测效率的缺点,在引入模糊决策思想下,提出一种将上述2种算法相结合的模糊决策异常检测算法。融合后的算法将2种算法的优势相结合,并用模糊决策方法提高算法的稳定性和适用性。通过在人工数据集和UCI数据集上的实验结果表明,该算法具有较好的异常检测效果。

This paper proposes a new idea using the idea of the fuzzy decision.And it is based on the kernelized spatial depth and the idea of the smallest sphere,intending for the problems that kernelized spatial depth function can not have good performance on some datasets and the parameters have the influence on the effectiveness.In this way,the algorithm improves the effectiveness and robustness in outlier detection by using the advantages of the algorithm and weakening the disadvantages.This paper does some experiments on the two artificial datasets and three different UCI datasets.Results show the effectiveness of the proposed idea.