A Survey of Evolutionary Algorithms for Decision-Tree Induction

By Rodrigo C. Barros, Márcio Porto Basgalupp, André Carlos Ponce de Leon Ferreira de Carvalho, Alex Alves Freitas. 2012

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In Artificial Intelligence

Citations in the corpus

Mathematical optimization in classification and regression trees (2021, methodology): “To enhance their performance, the greedy procedures were extended in different directions, such as the use of global optimization approaches (Barros et al. 2011; Fu et al. 2003; Grubinger et al. 2014).”
Survey on Classification Techniques for Data Mining (2015, background): “GainRatiosplit = Gainsplit SplitInfo ii) Pruning phase This phase avoids model over fitting [9].”
GENESIM: genetic extraction of a single, interpretable model (2016, methodology): “ocessing technique, that converts the ensemble of models into a single model, should result in a decision tree. A thorough survey of evolutionary algorithms for decision tree evolving can be found in [5]. Evolutionary algorithms for decision trees generate an initial population of decision trees, an”
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials (2018, background): “See Barros et al. (2012) for a survey on the use of EAs to fit decision trees.”
From evolutionary computation to the evolution of things (2015, methodology): “In other branches of machine learning, using EC to design algorithms has been shown to be very effective as an alternative to hand-crafting them, for instance, for inducing decision-trees 35 .”
End-to-End Learning of Decision Trees and Forests (2019, background): “Neural networks are currently the dominant classiﬁer in computer vision (Russakovsky et al. 2015; Cordts et al. 2016), whereas decision trees and decision forests have proven their worth when training data or computational resources are scarce (Barros et al. 2012; Criminisi and Shotton 2013).”
Recent advances in decision trees: an updated survey (2022, background): “Barros et al. (2012) provided a comprehensive survey on the usage of EAs for the induction of DTs.”
Classification Based on Decision Tree Algorithm for Machine Learning (2021, background): “The conceptual rules are much easier to construct than the numerical weights in the neural network of connections between nodes [37, 38].”
A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles (2017, methodology): “[11], genetic algorithms are discussed to construct decision trees from the data directly, in this paper, a genetic algorithm is applied on an ensemble of decision trees, created by using well-known induction algorithms combined with techniques such as bagging and boosting.”