Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

By Sreerama K. Murthy. 1998

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Citations in the corpus

Learning Optimal Decision Trees with SAT (2018, background): “…have been actively investigated since the early 80s [Breiman et al. , 1984; Quinlan, 1986; Quinlan, 1993; Mitchell, 1997; Cockett and Hierrera, 1990], and ﬁnd important practical applications in a wide range of domains, including ML and DM [Quinlan, 1993; Murthy, 1998; Rokach and Maimon, 2015].”
Building Decision Trees with Constraints (2003, methodology): “Among these proposals, decision tree classifiers (Murthy, 1998) have found the widest applicability in large-scale data mining environments.”
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.”
Fifty Years of Classification and Regression Trees (2014, unknown): “There have been previous reviews, but some are dated (e.g., Murthy, 1998) and others were written as brief overviews (e.g., Loh, 2008a; 2011; Merkle & Shaffer, 2009; Strobl et al., 2011) or simple introductions intended for non-statistics audiences (e.g., De’ath & Fabricius, 2000; Harper, 2003;…”
Supervised Machine Learning: A Review of Classification Techniques (2007, background): “Murthy (1998) provided an overview of work in decision trees and a sample of their usefulness to newcomers as well as practitioners in the field of machine learning.”
Recent advances in decision trees: an updated survey (2022, background): “Several great reviews have already been written on the subject, such as the ones by Murthy (1998), Rokach and Maimon (2007), Kotsiantis (2013) and Loh (2014), however, the last decade of research remains uncovered.”
A Survey on the Application of Genetic Programming to Classification (2010, methodology): “A decision tree [15] is composed of internal TABLE I CONFUSION MATRIX”
Global Induction of Oblique Decision Trees: An Evolutionary Approach (2005, background): “There exists dozens of decision tree induction algorithms [13].”
On static and dynamic methods for condensation-based privacy-preserving data mining (2008, methodology): “In many cases, a lot of relevant information for data mining algorithms such as classiﬁcation is hidden in the inter-attribute correlations [32].”
Finding Cost-Efficient Decision Trees (2014, methodology): “Further information about decision trees can be found in the survey by Murthy[28] and information about costsensitive decision tree induction algorithms can be found in the survey by Lomax and Vadera[24].”
Spectrum-Diverse Neuroevolution With Unified Neural Models (2017, unknown): “If we consider nodes in a DT to be like neurons (in neural trees, the DT’s nodes are exactly the same as neurons [28]–[30]), instead of processing information through some function and”