Fuzzy information entropy based on fuzzy relation in fuzzy rough set theory is an important metric of uncertainty. However, the research of fuzzy information entropy for hybrid feature outlier detection has not been reported. On this basis, this paper constructs a hybrid feature outlier detection method based on fuzzy information entropy by using fuzzy approximate space with fuzzy similarity relation. Firstly, the adaptive fuzzy radius of standard deviation and hybrid fuzzy similarity are employed to construct the fuzzy approximate space, and the relative fuzzy entropy is defined based on the fuzzy information entropy. Then, two kinds of metrics are constructed to describe the outlier degree of object. Finally, the fuzzy entropy-based outlier factor is integrated to implement outlier detection, and the relevant fuzzy information entropy-based outlier detection algorithm (FIEOD) is designed. The FIEOD algorithm is compared with the main outlier detection algorithms on public data. The experimental results reveal that the proposed method has better effectiveness and adaptability.

模糊粗糙集理论中基于模糊关系的模糊信息熵是不确定性的重要度量。然而，用于混合特征异常值检测的模糊信息熵的研究尚未见报道。在此基础上，本文利用具有模糊相似关系的模糊近似空间，构建了一种基于模糊信息熵的混合特征离群点检测方法。首先采用标准差自适应模糊半径和混合模糊相似度构建模糊近似空间，并根据模糊信息熵定义相对模糊熵。然后，构造了两种度量来描述对象的离群程度。最后，结合基于模糊熵的离群因子来实现离群点检测，并设计了相关的基于模糊信息熵的离群点检测算法（FIEOD）。FIEOD算法与公共数据上的主要异常值检测算法进行了比较。实验结果表明，该方法具有较好的有效性和适应性。