Induction of ripple-down rules applied to modeling large databases

By B. Gaines, P. Compton. 1995

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Citations in the corpus

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “The decision tree methods are representatives of the class of greedy and heuristic methods (e.g., [10,11,13,16,17,26–28, 31, 37, 38 , 51 ] ) that”
Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment (2008, methodology): “DT [26], Akaike’s information criterion (AIC) [34] and ridge shrinkage for LR [25], repeated incremental pruning to produce error reduction [28] and ripple-down rule learner [29] for RB.”
Do we need hundreds of classifiers to solve real world classification problems? (2014, methodology): “Ridor w implements the ripple-down rule learner (Gaines and Compton, 1995) with at least 2 instance weights.”
Graph-based approach for airborne light detection and ranging segmentation (2014, methodology): “Ridor w implements the ripple-down rule learner (Gaines and Compton, 1995) with at least 2 instance weights.”
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, background): “Ridor [18] is a rule-based decision tree technique where the decision tree consists of four nodes: a classification, a predicate function, a true branch, and a false branch.”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Greedy methods used in previous works (Malioutov and Varshney, 2013; Pollack et al., 1988; Friedman and Fisher, 1999; Gaines and Compton, 1995), where rules are added to the model one by one, do not generally produce high-quality sparse models.”