PRIE: a system for generating rulelists to maximize ROC performance

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Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “There is a large body of literature on interestingness measures for rules, such as lift, conviction, Laplace, and gain, and reviews of interestingness measures are given by Tan and Kumar [62], McGarry [44], Geng and Hamilton [24], and example methods are those of Simon et al [60], Fawcett [20].”
FURIA: an algorithm for unordered fuzzy rule induction (2009, background): “1An interesting probabilistic interpretation of rules in a rule list was recently proposed by Fawcett (2008).”
RuleMatrix: Visualizing and Understanding Classifiers with Rules (2018, methodology): “There exist various algorithms that can construct a list of rules from training data [11,23,45,47].”
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems (2009, background): “In their conventional definition, these algorithms can be applied to imbalanced classification problems, although the performance that they achieve is not the adequate, unless we use appropriate algorithms which are adapted to imbalanced performance measures [17].”