Add to ORKGIn this paper, the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection is presented. We show that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved using the Law of Large Numbers. A couple of counterexamples are presented to show that the original method is inconclusive and no quality certificate can be given, when using it as a means to detect anomalies. Hence, an alternative version of IRF is proposed, whose mathematical foundation, as well as its limitations, are fully justified. Finally, numerical experiments are presented to compare the performance of the classic IRF with the proposed on...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns pro...
Anomalies are data points that are few and different. As a result of these properties, we show that,...
The first successful isolation-based anomaly detector, ie, iForest, uses trees as a means to perform...
Most existing model-based approaches to anomaly detection construct a profile of normal instances, t...
This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (...
Anomalies are data points that are few and different. As a result of these properties, we show that,...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbou...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
Outlier Detection (OD) is a Pattern Recognition task which consists of finding those patterns in a s...
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent...
From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in ...
Outlier detection is an important research direction in the field of data mining. Aiming at the prob...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns pro...
Anomalies are data points that are few and different. As a result of these properties, we show that,...
The first successful isolation-based anomaly detector, ie, iForest, uses trees as a means to perform...
Most existing model-based approaches to anomaly detection construct a profile of normal instances, t...
This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (...
Anomalies are data points that are few and different. As a result of these properties, we show that,...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbou...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
Outlier Detection (OD) is a Pattern Recognition task which consists of finding those patterns in a s...
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent...
From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in ...
Outlier detection is an important research direction in the field of data mining. Aiming at the prob...
International audienceIn this paper, we propose DiFF-RF, an ensemble approach composed of random par...
Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns pro...
Anomalies are data points that are few and different. As a result of these properties, we show that,...