The KDF-IF algorithm redesigns the calculation of the anomaly score based on the Kernel Density Fluctuation (KDF) factor. By focusing on the density fluctuation of each instance neighborhood, the KDF draws the global, local, and clustered features for detecting the three types of anomalies.
Isolation forest is most popular and best technique for anomalies detection espicially for interpreation anomaly with DIFFI: Depth-based feature importance of ...
A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests · Nannan Dong, ...
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Apr 20, 2023 · Isolation Forests (IF) and improved algorithms about anomaly scores are commonly used to detect global, local, or clustered anomalies.
The key challenges in detecting anomalies include defining the precise boundaries between normal and abnormal behavior [3] , [4], [5]. The literature has ...
A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests. Inf. Sci ...
A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests · Author Picture ...
A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests · Nannan Dong ...
This paper proposes a method called Isolation Forest (iForest) which detects anomalies purely based on the concept of isolation without employing any distance ...
Missing: fluctuation | Show results with:fluctuation
The local anomaly factors for normal samples fluctuate around 1 and the local anomaly factors for anomalies are much larger than 1. This can distinguish ...