Identifying Multi-instance Outliers
Ou Wu, Jun Tao Gao, Weiming Hu, Bing Li, Mingliang Zhu · 2010
This paper studies a new data mining problem called multi-instance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types of multi-instance outliers. Two general identification approaches are proposed based on the state-of-the-art (single-instance) outlier detector LOF (local outlier factor). One approach utilizes the underlying mechanism of the kernel method and plunges the set distance into LOF to detect the multi-instance outliers. The other approach takes each instance's neighborhood into account. Based on the two approaches, four concrete multi-instance outlier detectors are then introduced. We conduct experiments over four synthetic data collections and three real-world data collections (two Musk data sets [22, 23] and a hard-drive inspection data set [24]). The experimental results show that the proposed multi-instance outlier detectors are effective while the algorithms that ignore the multi-instance settings perform poorly. Especially, the results on the two Musk sets are consistent with the multi-instance learning results; the results on the hard-drive inspection data set demonstrate that multi-instance outlier identification is promising for real applications.