The Attribute Selection Problem in Decision Tree Generation

By Usama M. Fayyad, K. Irani. 1992

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

Mathematical optimization in classification and regression trees (2021, methodology): “RFs, as well as other methods combining tree models, give, in general, better accuracies than single greedy trees (Fernández-Delgado et al. 2014).”
A System for Induction of Oblique Decision Trees (1994, background): “Many di erentmeasures of impurity have been studied (Breiman et al., 1984; Quinlan, 1986; Mingers,1989b; Buntine & Niblett, 1992; Fayyad & Irani, 1992; Heath et al., 1993b).”
Top-down induction of decision trees classifiers - a survey (2005, background): “Fayyad and Irani [17] have presented the orthogonality (ORT) criterion.”
Optimal instance selection for improved decision tree (2007, methodology): “Comparative studies of the splitting criteria have been conducted by several researchers during the last thirty years, such as Baker and Jain (1976), Fayyad and Irani (1992), Loh and Shih (1997), and Lim et al. (2000).”
Multivariate Decision Trees (1995, background): “Breiman, et al. (1984), Quinlan (1986a), Mingers (1989a), Safavian and Landgrebe (1991), Buntine and Niblett (1992) and Fayyad and Irani (1992b) discuss and compare diierent partition merit criteria.”
Oblique Multicategory Decision Trees Using Nonlinear Programming (2005, background): “Limitations of purity-based optimization criteria were addressed by Fayyad and Irani (1992).”