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Automatic optimization of outlier detection ensembles using a limited number of outlier examples
2020
International Journal of Data Science and Analytics
In data analysis, outliers are deviating and unexpected observations. Outlier detection is important, because outliers can contain critical and interesting information. We propose an approach for optimizing outlier detection ensembles using a limited number of outlier examples. In our work, a limited number of outlier examples are defined as from 1 to 10% of the available outliers. The optimized outlier detection ensembles consist of outlier detection algorithms, which provide an outlier score
doi:10.1007/s41060-020-00222-4
fatcat:ng5esoj47zch3aswm252wcbykq