Outlier Handling
Outlier Handling is a data preprocessing technique in feature engineering that identifies and mitigates the influence of anomalous observations deviating significantly from the majority of data points to improve model robustness and accuracy.
3.3K
Publications
266.2K
Citations
8K
Authors
2.4K
Institutions
Publications per year
2017–2026
1.2K
3.3K
A Survey of Outlier Detection Methodologies
Jim Austin · Artificial Intelligence Review · 2004 · 3.3K citations · Full text
1–5 of 3.3K
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London School of Economics and Political Science, United Kingdom
15 publications · 2.8K citations
15
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14
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13
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2.4K
KU Leuven
Leuven, Belgium
27 h-index
91 publications
8K citations

University of Minnesota
Minneapolis, United States
24 h-index
48 publications
3.7K citations
Carnegie Mellon University
Pittsburgh, United States
24 h-index
63 publications
5.4K citations

Northwestern Polytechnical University
Xi'an, China
22 h-index
81 publications
3.8K citations

University of British Columbia
Vancouver, Canada
22 h-index
55 publications
8.9K citations

Chinese Academy of Sciences
Beijing, China
22 h-index
82 publications
4.4K citations

Xi'an Jiaotong University
Xi'an, China
21 h-index
78 publications
3.4K citations
Wuhan University
Wuhan, China
21 h-index
100 publications
5.2K citations
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73 publications · 24.1K citations
45
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72 publications · 8.2K citations
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TPAMI · 43 publications · 9.3K citations
36
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Repository · 88 publications · 4.3K citations
29
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47 publications · 2.1K citations
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