Identifying Multi-instance Outliers

This paper studies a new data mining problem called multiinstance 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 multiinstance 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 multiinstance 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.