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The first algorithm for random decision forests was created in 1995 by Tin Kam Ho [1] using the random subspace method, [2] which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.
Download as PDF; Printable version; ... when random forest is used to fit models, jackknife estimated variance is defined as: ... The results shows in paper ...
Rotation forest – in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features. [ 13 ] A special case of a decision tree is a decision list , [ 14 ] which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a ...
There are several important factors to consider when designing a random forest. If the trees in the random forests are too deep, overfitting can still occur due to over-specificity. If the forest is too large, the algorithm may become less efficient due to an increased runtime. Random forests also do not generally perform well when given sparse ...
Fast algorithms such as decision trees are commonly used in ensemble methods (e.g., random forests), although slower algorithms can benefit from ensemble techniques as well. By analogy, ensemble techniques have been used also in unsupervised learning scenarios, for example in consensus clustering or in anomaly detection.
An ensemble of models employing the random subspace method can be constructed using the following algorithm: Let the number of training points be N and the number of features in the training data be D. Let L be the number of individual models in the ensemble. For each individual model l, choose n l (n l < N) to be the number of input points for l.
Tin Kam Ho (Chinese: 何天琴) is a computer scientist at IBM Research with contributions to machine learning, data mining, and classification.Ho is noted for introducing random decision forests in 1995, and for her pioneering work in ensemble learning and data complexity analysis.
Isolation Forest is an algorithm for data anomaly detection using binary trees. It was developed by Fei Tony Liu in 2008. [ 1 ] It has a linear time complexity and a low memory use, which works well for high-volume data.