Fairlof: fairness in outlier detection
WebAug 29, 2024 · We propose a fair outlier detection method, FairLOF, that is inspired by the popular LOF formulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced within LOF and develop three heuristic principles to enhance fairness, which form the basis of the FairLOF method. WebFairLOF operates on the original feature space, which limits its capacity towards detection accuracy and fairness degree. Since the ... outlier detection by proposing two fairness …
Fairlof: fairness in outlier detection
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WebJun 12, 2024 · LOF. In Local Outlier Factor (LOF), the idea revolves around the concept of local regions.Here, we calculate and compare the local density of the focus point with the local density of its neighbours. If we find that the local density of the focus point is very low compared to its neighbours, that would kind of hint that the focus point is isolated in that … WebApr 14, 2024 · Existing work in outlier detection regards being an outlier as a binary property. ... The objective of bandwidth degradation is to achieve both intra-class and inter-class utility fairness in each ...
WebDec 1, 2024 · An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed toward particular groups … WebFairLOF (Deepak and Abraham 2024, 2024) focuses on automated group fair outlier detection, developing a technique that extends LOF (discussed above) for fairness. FairLOF adapts LOF to incorporate adjustments based on protected attribute memberships of the object in question and its neighbors, to ensure that protected groups are fairly ...
WebAnother efficient way to perform outlier detection on moderately high dimensional datasets is to use the Local Outlier Factor (LOF) algorithm. The neighbors.LocalOutlierFactor (LOF) algorithm computes a score (called local outlier factor) reflecting the degree of abnormality of the observations. WebFairLOF: Fairness in Outlier Detection. Data Science and Engineering, 6 (4), 485-499. ... Span Detection for Kinematics Word Problems. Paper presented at The 29th International Conference on Neural Information Processing (ICONIP 2024), IIT …
WebMay 20, 2024 · We develop an outlier detection method, called FairLOF, inspired by the construction of LOF and makes use of thre e principles to nudge the out- lier …
WebFairLOF operates on the original feature space, which limits its capacity towards detection accuracy and fairness degree. Since the original feature space often does not expose much information on the underlying data pattern, it adds difficulty to … maite bushwickWebOct 15, 2015 · The PROP method falsely identified what its authors call “clear outliers” 68.7% to 88.7% of the time, and detected additional samples as having what the authors call “potential outliers.” Note that these results are no indication of the abilities of these two outlier detection methods under their specified assumption of normality. mait edvinsson hedemoraWebFairLOF: Fairness in Outlier Detection, Data Sc. and Engg. J. 2024, On Fairness and Interpretability, Workshop on AI for Social Good (AI4SG), 2024, Ethical Considerations in Data-driven Fake News Detection, in Data Science for Fake News, Springer 2024, Whither Fair Clustering?, AI for Social Good Workshop (AI4SG), 2024 maite herrera