Data Mining with Decision Trees: Theory and Applications

By Lior Rokach, Oded Z. Maimon. 2007

810 citations recorded by the source corpus.

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In Information Systems

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Many applications of these models fall out of the fields of computation and AI (even information technologies), meaning that experts from other fields usually feel comfortable interpreting the outputs of these models [183, 184, 185].”
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.”
Information gain ratio correction: Improving prediction with more balanced decision tree splits (2018, background): “ike Quinlan’s gain ratio), the reasons for this normalization are given below in Section 3. That is the case of the Distance Measure LopezDeMantras (1991), it normalizes the goodness-of-split measure Rokach (2008) in a similar way that the gain ratio does for the information gain. There is also the ”
Neural Random Forests (2016, background): “The monographs by Breiman et al. (1984), Devroye et al. (1996), Rokach and Maimon (2008), and Hastie et al. (2009) will provide the reader with introductions to the general subject area, both from a practical and theoretical perspective.”
Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold (2012, methodology): “While the Adaboost algorithm using decision tree has not previously been applied in geomorphology, the idea of an ensemble of decision trees (also known as a decision forest (Rokach and Maimon, 2008)) has been examined in the past (Saito et al., 2009; Kuhnert et al., 2010).”
Data Mining Methods for Recommender Systems (2015, background): “Decision trees [61, 63] are classifiers on a target attribute (or class) in the form of a tree structure.”
Congestive heart failure detection using random forest classifier (2016, background): “The accuracy is then calculated by [40]:”