Top-down induction of decision trees classifiers - a survey

By Lior Rokach, Oded Z. Maimon. 2005

872 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

Interpretable & Explorable Approximations of Black Box Models (2017, background): “Examples of such models include decision trees [9], decision lists [6], decision sets [4], linear models, generalized additive models [7] etc.”
Faithful and Customizable Explanations of Black Box Models (2019, background): “Explaining Model Behavior: One approach for interpretability is learning predictive models which are human understandable (e.g., decision trees (Rokach and Maimon 2005), decision lists (Letham et al. 2015), decision sets (Lakkaraju, Bach, and Leskovec 2016), linear models, generalized additive…”
HHCART: An oblique decision tree (2015, background): “Most of the oblique DT induction algorithms construct DTs in a top-down fashion [18].”
Carat: collaborative energy diagnosis for mobile devices (2013, background): “This section describes how to generate a diagnosis for an anomaly, which involves building a tree structure similar to a classification or decision tree [24, 39], and conclude with an example.”
Good Practice in Large-Scale Learning for Image Classification (2014, background): “The reader may refer to [63] for an extended survey of top-down approaches for classification.”
Budding Trees (2014, background): “I. INTRODUCTION A decision tree is a hierarchical structure for supervised learning tasks, composed of internal decision nodes and terminal label nodes [1]–[3].”
Classification Based on Decision Tree Algorithm for Machine Learning (2021, neutral): “5), Classification And Regression Tree(CART) [44], CHi-squared Automatic Interaction Detector(CHAID) [45], Multivariate Adaptive Regression Splines (MARS) [46], Generalized, Unbiased, Interaction Detection and Estimation (GUIDE), Conditional Inference Trees (CTREE) [47],[48], Classification Rule wit”
Ensemble-based classifiers (2010, background): “9 represents any top-down decision tree induction algorithm (Rokach and Maimon 2005a) with the following modification: the decision tree is not pruned and at each node, rather than choosing the best split among all attributes, the inducer randomly samples N of the attributes and choose the best…”
Machine Learning Algorithms: A Review (2022, neutral): “Pseudo code for AdaBoost [13] Fig-8: Pseudo code for Bagging [13] The other popular ensembles methods are [3]:”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, methodology): “It is important to stress that comprehensive surveys on decision trees have been previously published, such as the papers by Safavian and Landgrebe [24], Murthy [25], and Rokach and Maimon [26].”
Theory-Guided Machine Learning in Materials Science (2016, positive): “In the second case where entropy , or information gain (Rokach and Maimon, 2005), is used instead, neither the GII nor the tolerance factor appear as factors, but new factors appear, such as the difference in ionic radii ratios the A , B , and X = O ions as [( r A / r O ) − ( r B / r O )] and A…”
Adaptive Hypergraph Learning and its Application in Image Classification (2012, neutral): “Many classification methods have been developed, such as decision tree [1], -nearest neighbor, and support vector machines (SVMs) [2], [3].”
Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification (2017, methodology): “A workflow model for review processing was established to compare Naïve Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression classifiers.”
CatBoost: unbiased boosting with categorical features (2017, methodology): “A decision tree [4, 10, 20] is a model built by a recursive partition of the feature space Rm .”