Functional Trees

By João Gama. 2001

295 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, methodology): “In terms of searching, algorithms such as decision tree-based methods [86, 85] and genetic algorithms [87] require a pre-defined operation space, while annotation-based approaches do not, as the latter use domain knowledge (in”
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, background): “5 [43], functional tree (FT) [44], Bayes net [45], Naive-Bayes [46], and K-nearest neighbors (K-NN) [47] classifiers that are available at Waikato environment for knowledge analysis software [48].”
Decision trees: a recent overview (2011, methodology): “Gama (2004) combines a univariate decision tree with a linear function by means of constructive induction.”
Policy Tree: Adaptive Representation for Policy Gradient (2015, background): “This kind of representation has been studied in regression and classification scenarios (Gama 2004), but not in reinforcement learning to our knowledge.”
Decoding brain states from fMRI connectivity graphs (2011, methodology): “It should be emphasised that the feature selection procedure is based on graphs from the training set only: in a leave-one-subject-out cross-validation paradigms, only data from N − 1 subjects is used to learn which connections are statistically signi ﬁ cant, and the computed “ signi ﬁ cance mask ”…”
Decision trees for mining data streams (2006, unknown): “The use of functional leaves and functional nodes for classiﬁcation and regression has been studied in [11].”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, methodology): “The performance of the FT model depends on the minimum number of instance per leaf, the number of bootstrap iterations and the selection of function trees (Gama 2004).”
Understanding Random Forests: From Theory to Practice (2014, background): “te, decision trees. In this setting however, decomposition 3.16 no longer holds, hence making the search of the best split often more computationally intensive. More recently, several authors (e.g., [Gama, 2004 ; Criminisi and Shotton, 2013 ; Botta, 2013 ]) outlined more general frameworks in which ”
Detecting click fraud in online advertising: a data mining approach (2014, methodology): “Type Method Description Single FT tree A type of decision trees with logistic regression functions at the (Gama, 2004) inner nodes and/or leaves REP tree A decision tree is built using information gain or variance and (Su and Zhang, 2006) then pruned using reduced-error pruning (REP) method Decision”
The WEKA data mining software: an update (2009, unknown): “• Functional trees [12]: decision trees with oblique splits and linear functions at the leaves.”
Fifty Years of Classification and Regression Trees (2014, background): “Yildiz & Alpaydin (2001, 2005a, 2005b) and Gama (2004) compared linear splitting with linear node modeling.”