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Cited by 49 publications
(14 citation statements)
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References 41 publications
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“…Note that the neighbor ranking based methods are sensitive to parameter k of models, it is difficult to choose the right k for different applications. To cope with this problem, Ha et al 16 provided a heuristic strategy to select the value of k, along with an iterative random sampling procedure. The underlying assumption is that outlying objects are less likely selected than inlying objects in random sampling, and therefore, more inlierness scores should be given to the selected objects in each sampling.…”
Section: Neighbor Ranking-based Methodsmentioning
confidence: 99%
“…Note that the neighbor ranking based methods are sensitive to parameter k of models, it is difficult to choose the right k for different applications. To cope with this problem, Ha et al 16 provided a heuristic strategy to select the value of k, along with an iterative random sampling procedure. The underlying assumption is that outlying objects are less likely selected than inlying objects in random sampling, and therefore, more inlierness scores should be given to the selected objects in each sampling.…”
Section: Neighbor Ranking-based Methodsmentioning
confidence: 99%
“…In addition, assigning the right value to k for a specific application is not trivial. To this end, Ha et al [33] adopted a heuristic strategy to select an appropriate value for k using an iterative random sampling procedure.…”
Section: Neighbour-based Detectionmentioning
confidence: 99%
“…Many improved outlier detection algorithms intuitively give the size of the nearest neighbor parameter k, but where the parameters come from is ambiguous. To improve the accuracy of the algorithm and reduce the influence of parameter selection, many outlier detection algorithms have been proposed [21][22][23][24][25]. Ha et al proposed a new outlier detection method including iterative random sampling.…”
Section: Parameter Selection In Outlier Detectionmentioning
confidence: 99%