Unifying Instance-Based and Rule-Based Induction

By Pedro M. Domingos. 1996

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Citations in the corpus

Interpretable Two-level Boolean Rule Learning for Classification (2015, neutral): “, 2012): covering (Rivest, 1987; Clark & Niblett, 1989; Cohen, 1995; Fürnkranz, 1999), bottom-up ( Salzberg, 1991; Domingos, 1996), multi-phase ( Liu et al.”
Boolean Decision Rules via Column Generation (2018, background): “see also the survey [30]) of learning rules one by one and removing “covered” examples, a bottomup strategy of combining more specific rules into more general ones [45, 22, 41], and associative classification in which association rule mining is followed by rule selection using various criteria [38, ”
Reduction Techniques for Instance-Based Learning Algorithms (2000, methodology): “…1991; Wettschereck & Dietterich, 1995) use hyperrectangles to represent collections of instances; instances can be generalized into rules (Domingos, 1995, 1996); and prototypes can be used to represent a cluster of instances (Chang, 1974), even if no original instance occurred at the…”
Learning sparse two-level boolean rules (2016, background): “The bottom-up strategy [12] successively combines more speciﬁc clauses into more general clauses according to local criteria like pairwise similarity.”
Mining knowledge from text using information extraction (2005, methodology): “A variant of Domingos’ RISE algorithm [26], an elegant approach to unifying rule-based and instance-based methods.”
Support vector machines with symbolic interpretation (2002, methodology): “Once extracted the rules, the system classifies an example by assigning it the class of the nearest rule in the knowledge base (following the nearest-neighbor philosophy) by using a distance metric [5], [9].”
Instance-based learning in dynamic decision making (2003, methodology): “For example, Domingos (1996) attempts to combine rule induction and instance-based approaches generalizing rules from instances in a gradual process of classification.”
ENDER: a statistical framework for boosting decision rules (2010, methodology): “There are also several rule-based approaches of lazy learning type (Bazan 1998; Góra and Wojna 2002b), possibly combined with instance-based methods (Domingos 1996; Góra and Wojna 2002a).”
Benchmarking Least Squares Support Vector Machine Classifiers (2004, methodology): “Considering the Average Accuracy (AA) and Average Ranking (AR) over all domains (Bay, 1999; De Groot, 1986; Domingos, 1996 ), the RBF SVM gets the best average accuracy and the RBF LS-SVM yields the best average rank.”