Abstract
We propose a novel method for detecting outliers based on the leave-one-out density. The leave-one-out density of a datum is defined as a ratio of the number of data inside a region to the volume of the region after the datum is removed from an original data set. We propose an efficient algorithm that evaluates the leave-one-out density of each datum on a set of regions around the datum by using binary decision diagrams. The time complexity of the proposed method is near linear with respect to the size of a data set, while the outlier detection accuracy is still comparable to other methods. Experimental results show the usefulness of the proposed method.
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Kutsuna, T., Yamamoto, A. (2014). Outlier Detection Based on Leave-One-Out Density Using Binary Decision Diagrams. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_40
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DOI: https://doi.org/10.1007/978-3-319-06605-9_40
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