Induction of Decision Trees

By J. Ross Quinlan. 1986

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Citations in the corpus

A Hybrid Approach to Privacy-Preserving Federated Learning (2018, methodology): “This is the same metric used in the ID3 [34],”
SAT-Based Encodings for Optimal Decision Trees with Explicit Paths (2020, background): “Greedy algorithms for learning decision trees based on recursive splitting are well-known [6,32,33]; see also [8] for an overview.”
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels (2019, background): “Label noise is an important issue and has long been researched [1, 17].”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, methodology): “DECISIONTREE [51]: We use decision trees with pruning as an alternative classification method.”
Classification and prediction of diabetes disease using machine learning paradigm (2020, background): “A decision tree is a tree structure based model which describes the classification process based on input features [41].”
End-to-End Learning of Decision Trees and Forests (2019, background): “We develop a new probabilistic split criterion that generalizes the long-established information gain (Quinlan 1990).”
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning (2019, background): “Decision splits are made based on impurity and information gain [20].”
Optimization of Tree Ensembles (2017, methodology): “anion). We give concluding remarks in Section 8. 2. Literature review Decision tree models became popular in machine learning with the introduction of two algorithms, ID3 (iterative dichotomiser; see Quinlan 1986) and CART (classication and regression tree; see Breiman et al. 1984). Decision tree mo”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “Small disjuncts are rules that cover a small cluster of examples resulting from concepts that are underrepresented [46, 85, 86].”
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums (2008, methodology): “Furthermore, the utilization of feature selection techniques such as genetic algorithms [Holland 1975] and information gain [Shannon 1948; Quinlan 1986] is also included to improve classiﬁcation accuracy and get insight into the important features for each sentiment class.”