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3 days ago · The Isolation Forest detects anomalies based on the degree of isolation between outliers and normal values in the feature space by constructing multiple random trees. Owing to its independence from data distribution assumptions and capability to handle high-dimensional and non-Gaussian data, this method is particularly effective for detecting anomalies within complex datasets [ 29 ].
1 day ago · Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422. Digital Library.
19 hours ago · Isolation Forest: An ensemble method that isolates anomalies by creating decision trees that split the data into smaller subsets. Anomalies are detected by flagging any value that is isolated in a small subset. Local Outlier Factor (LOF): An ensemble method that detects anomalies by calculating the local density of the data.
4 days ago · The isolation forest method constructs a tree to identify such outliers genes. This procedure can be applied for multiple iterations. This procedure can be applied for multiple iterations. From the isolation trees, we can identify samples that deviate from their respective groups and pinpoint the genes responsible for such discrepancy and thus remove them from the gene set.
3 days ago · The study establishes missing value filling based on improved clustering algorithm and outlier identification based on improved isolation forest algorithm to identify outliers and fill in missing ...
5 days ago · When a forest of random trees collectively produce short path lengths for isolating some particular samples, they are highly likely to be anomalies and the measure of normality is close to 0. Similarly, large paths correspond to values close to 1 and are more likely to be inliers.
5 days ago · Deep Optimal Isolation Forest with Genetic Algorithm for Anomaly Detection. ICDM 2023: 678-687
