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This method computes a consistency value for each combination of k and t, and the optimal values of k and t are chosen by maximizing this consistency value. The ...
which is designed to select the optimal parameters t and k for neighborhood-based outlier detectors. Unlike previous methods that determine the optimal ...
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How to choose k in local outlier factor?
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This article proposes neighborhood consistency, a new concept which tackles the existing issues of selecting the optimal k by considering both application and ...
The threshold for detection is determined by the distribution of the neighbor distances and the value of the Detection Sensitivity parameter. You can ...
Feb 21, 2015 · The authors of the paper recommend choosing a minimum k and a maximum k, and for each point, taking the maximum LOF value over each k in that ...
Feb 22, 2021 · Seeing how the LOF algorithm, it does not seem to me that there is a "optimal" way to select a value of "k". It seems that you must refer to the ...
ability of uncertainty data with outlier detection, and detects an outlier in the selected subspaces based on the majority class in the neighborhood of the data ...
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Aug 30, 2020 · Local outlier factor (LOF) values identify an outlier based on the local neighborhood. It gives better results than the global approach to ...
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Ghosh [1] investigated a rule for selecting the optimum value of k by estimating the misclassification rate of the nearest neighbor classifier. Ha et al. [20] ...
The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with ...
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