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- W4301374003 abstract "Isolation Forest has a low computational complexity, hence has been widely applied to detect outliers in large-scale data. However, it suffers from the artifacts caused by the hyperplanes chosen, thereby failing to detect outliers in some specific regions. To tackle this problem, we propose the random-projection-based Isolation Forest, which works in two steps. First, we transform the data using the random projection technique. Then, we employ the Isolation Forest to identify outliers using the transformed data. Experimental results show that the proposed methods outperform 12 state-of-the-art outlier detectors." @default.
- W4301374003 created "2022-10-05" @default.
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- W4301374003 date "2022-11-01" @default.
- W4301374003 modified "2023-10-01" @default.
- W4301374003 title "Sparse random projection isolation forest for outlier detection" @default.
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- W4301374003 doi "https://doi.org/10.1016/j.patrec.2022.09.015" @default.
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