Unsupervised Learning With Random Forest Predictors

By T. Shi, Steve Horvath. 2006

528 citations recorded by the source corpus.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d3b6e1135ccc5f9009eb1d025b445fa185893656

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Citations in the corpus

Isolation Forest (2008, methodology): “In the first experiment we compare iForest with ORCA [3], LOF [6] and Random Forests (RF) [12].”
Isolation-Based Anomaly Detection (2012, methodology): “This research was supported by the Australian Postgraduate Awards (APA) and the Information and Communications Technologies (ICT) Postgraduate Research Scholarships, the National Science Foundation of China (61073097, 61021062), the National Fundamental Research Program of China (2010CB327903)…”
Anomaly Detection for IoT Time-Series Data: A Survey (2020, methodology): “2) Clustering: Lui et al. [93], [94] presented an ensemble method utilizing isolation trees (termed isolation forests) to perform the anomaly detection, this is compared to a number of other contemporary methods, including ORCA [95], one-class SVM [96], LOF [97], and random forests [98].”
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable (2016, background): “RF proximity has found a variety of applications, including clustering [46], outlier detection [53], imputation [47], etc.”
Random Forests for Genomic Data Analysis (2012, unknown): “Shi and Horvath [58] further investigated the properties of RF dissimilarity using simulations and recommended that randomly sampling from the product of themarginal distributions of the variables”
Random forest-based similarity measures for multi-modal classification of Alzheimer's disease (2013, unknown): “Random forest-derived similarities have been successfully applied in unsupervised clustering tasks, for example those involving high-dimensional genetic or tissue microarray data (Shi and Horvath, 2006; Shi et al., 2005).”
Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection (2016, methodology): “As an alternative, some other clustering methods can be considered to improve the performance, such as random forest clustering [45].”