document: Feature bagging for outlier detection Outlier detection has recently become an important problem in many industrial and financial applications . In this paper , a novel feature bagging approach for detecting outliers in very large , high dimensional and noisy databases is proposed . It combines results from multiple outlier detection algorithms that are applied using different set of features . Every outlier detection algorithm uses a small subset of features that are randomly selected from the original feature set . As a result , each outlier detector identifies different outliers , and thus assigns to all data records outlier scores that correspond to their probability of being outliers . The outlier scores computed by the individual outlier detection algorithms are then combined in order to find the better quality outliers . Experiments performed on several synthetic and real life data sets show that the proposed methods for combining outputs from multiple outlier detection algorithms provide non-trivial improvements over the base algorithm .
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