Rotation Forest: A New Classifier Ensemble Method

By Juan José Rodríguez Diez, Ludmila I. Kuncheva, Carlos J. Alonso. 2006

1,971 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Forest Floor Visualizations of Random Forests (2016, neutral): “Therefore all variations are compatible with the forest floor methodology.”
Graph-based approach for airborne light detection and ranging segmentation (2014, methodology): “RotationForest w (Rodŕıguez et al., 2006) uses J48 as base classifier, principal component analysis filter, groups of 3 inputs, pruning confidence C=0.”
On Oblique Random Forests (2011, background): “Our approach is also related to “rotation forests” [33] where oblique split directions are sought from the principal components of feature subsets of the training data, reportedly improving results significantly in selected classification tasks.”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, background): “Moreover, it has been appointed as one of the top-10 Data Mining algorithms [56] and it is considered as a standard to build accurate ensembles [34,45,40].”
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, methodology): “, rotation forest [35] and bagging [36].”
Ensemble learning: A survey (2018, methodology): “Rotation forest (Rodriguez, Kuncheva, & Alonso, 2006) is a method that generates diversity among decision tree inducers by training each inducer on the whole dataset in a rotated feature space.”
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, methodology): “Several extensions of bagging and boosting have been proposed in the literature (e.g., Breiman, 2001; Friedman, 2002; Rodriguez, et al., 2006).”
Decision trees: a recent overview (2011, methodology): “Based on Principal Component Analysis (PCA), Rodríguez et al. (2006) proposed a new ensemble classifier generation technique Rotation Forest.”
Meta Net: A New Meta-Classifier Family (2013, neutral): “Meta-Rotation Forest (Rodriguez et al. 2006).”
Detecting click fraud in online advertising: a data mining approach (2014, methodology): “Features are split (Rodrguez et al., 2006) into subsets and principal component analysis is applied to each Tree ensemble Variant of random forest whereby each tree model is learned on (Berthold et al.”
Ensemble approaches for regression: A survey (2012, methodology): “…ANN preparing ensembles Nearest Neighbor Ensemble [Domeniconi and Yan 2004] KNN BEN Bootstrap Ensemble with Noise, or [Raviv and Intrator 1996] DT Input smearing [Frank and Pfahringer 2006] Ensembles based on RSBRA [Cai and Wu 2008] SVM Rotation forests [Rodr´iguez et al. 2006] DT [Zhang et al.”
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “…implementations of k Nearest Neighbour (where k is set through cross validation), Naive Bayes, C4.5 decision tree [25], Support Vector Machines [26] with linear and quadratic basis function kernels, Random Forest [27] (with 100 trees), Rotation Forest [28] (with 10 trees) and a Bayesian network.”
Local Decorrelation For Improved Pedestrian Detection (2014, background): “Recently there has been renewed interest in random forests with oblique splits [23] [30] and Marin et al. [20] even applied such a technique to pedestrian detection.”
KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, background): “Nowadays, many researchers base their work on DM tools [42], or they employ tools specifically designed for an area of DM, such as [53].”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, methodology): “Rotation Forest is an ensemble technique developed by Rotation Forest is an ensemble technique developed by Rodriguez et al. [95] to improve the predictive capability of single classifiers. predictive capability of single classiﬁers.”