A comparative evaluation of outlier detection algorithms: Experiments and analyses
By Rémi Domingues, Maurizio Filippone, Pietro Michiardi, Jihane Zouaoui. Pattern Recognition, 2018
432 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/62d43f751659e7bd1f869768e7f392da68d7ee15
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wojciech Samek
- Grgoire Montavon
- K. Müller
- Klaus‐Robert Müller
- Sebastian Lapuschkin
- Marius Kloft
- Xiyang Hu
- Songqiao Han
In Control and Systems Engineering
- Thomas G. Dietterich
- Grgoire Montavon
- Yue Zhao
- Mohamed Hammad
- Wojciech Samek
- Klaus‐Robert Müller
- Marius Kloft
- Mohamed Jaward Bah
In Statistics and Probability
- Thomas G. Dietterich
- Grgoire Montavon
- Yue Zhao
- Mohamed Hammad
- Wojciech Samek
- Songqiao Han
- Hailiang Huang
- Klaus‐Robert Müller
Citations in the corpus
- Progress in Outlier Detection Techniques: A Survey (2019, background): “Researchers have provided several adopted measurements to evaluate outlier detection algorithm performance [223].”
- A Unifying Review of Deep and Shallow Anomaly Detection (2020, methodology): “OOD [423], HCP/BRATS [52], Neuropathology [60], [424] Security: Credit-card-fraud [425], URL [426], UNSWNB15 [427] Time series: NAB [428], Yahoo [429] Misc.: Emmott [418], ELKI [430], ODDS [431], UCI [432], [433] In a second step, we will look at the limitations of classical evaluation techniques, s”
- ADBench: Anomaly Detection Benchmark (2022, background): “There are some notable works that take effort to benchmark AD methods on tabular data, e.g., [25, 38, 42, 150, 166] (see Appx.”