PRISM: An Algorithm for Inducing Modular Rules

By Jadzia Cendrowska. 1987

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Citations in the corpus

Scalable Bayesian Rule Lists (2016, methodology): “Rule learning algorithms have been developed possibly since the AQ algorithm in the 1960s (Michalski, 1969) and throughout the 1980’s and 90’s (Cendrowska, 1987; Quinlan, 1983; Clark & Niblett, 1989; Cohen, 1995; Liu, Hsu, & Ma, 1998).”
Induction of ripple-down rules applied to modeling large databases (1995, methodology): “There are some minor internal refinements to cope with known problems of empirical induction as described by Quinlan (1987) and Cendrowska (1987). For example, the lack of performance gain by ‘exclusive-or’ conditions is dealt with by proceeding with clause addition on a provisional basis even if th”
Predicting phishing websites based on self-structuring neural network (2013, background): “5 [16], PRISM [17], PART [9] and JRip [9].”
Separate-and-Conquer Rule Learning (1999, methodology): “PRISM (Cendrowska 1987) for example evaluates rules with a variant of the information content heuristic and breaks ties using positive coverage.”
A SAT-Based Approach to Learn Explainable Decision Sets (2018, unknown): “This approach may result in smaller and, thus, better interpretable solutions, in which case it would be appealing to compare it again heuristic rule-based classifiers targeting this same problem, e.g. CN2 [6, 7] and PRISM [5] among others.”
A statistical approach to rule learning (2006, methodology): “DNF rule sets are induced for example by PRISM (Cendrowska, 1987) and RIPPER (Cohen, 1995).”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “3 PRISM PRISM [47] is a method that generates rulesets for each class present in training data.”
Graph-based approach for airborne light detection and ranging segmentation (2014, unknown): “, 1999); Prism (Cendrowska, 1987); ThresholdSelector; VotedPerceptron (Freund and Schapire, 1998) and Winnow (Littlestone, 1988).”
Do we need hundreds of classifiers to solve real world classification problems? (2014, background): “We could not include in our collection the remaining 9 classifiers: ADTree, alternating decision tree (Freund and Mason, 1999); AODE, aggregating one-dependence estimators (Webb et al., 2005); Id3 (Quinlan, 1986); LBR, lazy Bayesian rules (Zheng and Webb, 2000); M5Rules (Holmes et al., 1999); Prism ”
Class Noise vs. Attribute Noise: A Quantitative Study (2003, methodology): “We have used ﬁve classiﬁcation algorithms, C4.5 (Quinlan 1993), C4.5 rules (Quinlan 1993), HCV (Wu 1995), 1R (Holte 1993) and Prism (Cendrowska 1987) in our experiments.”
Comprehensible classification models: a position paper (2014, background): “Hence, some branch(es) may have to be added to the tree just to preserve the structure of a tree, even if that branch is associated with an irrelevant attribute value [9], [10], [24], [29].”
On expressiveness and uncertainty awareness in rule-based classification for data streams (2017, unknown): “The original Prism algorithm [12] only works on categorical ttributes and produces rule terms of the form (α = ν) .”
Learning under Concept Drift: A Review (2019, methodology): “Instead of inducing rules from decision trees, [138] proposed another decision rule algorithm based on PRISM [139] to directly induce rules from data.”