34 citations · 73 across the 6 of their papers we have counts for
6 papers
One Class Splitting Criteria for Random Forests
Nicolas Goix, Nicolas Drougard, Romain Brault +1
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setti…
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
Nicolas Goix
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performan…
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Extremes play a special role in Anomaly Detection. Beyond inference and simulation purposes, probabilistic tools borrowed from Extreme Value Theory (EVT), such as the angular measu…
Sparsity in Multivariate Extremes with Applications to Anomaly Detection
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e.g. portfolio…
Learning the dependence structure of rare events: a non-asymptotic study
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Assessing the probability of occurrence of extreme events is a crucial issue in various fields like finance, insurance, telecommunication or environmental sciences. In a multivaria…
On Anomaly Ranking and Excess-Mass Curves
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Learning how to rank multivariate unlabeled observations depending on their degree of abnormality/novelty is a crucial problem in a wide range of applications. In practice, it gene…