From Local Patterns to Global Models: The LeGo Approach to Data Mining

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Interpretable Two-level Boolean Rule Learning for Classification (2015, neutral): “Keywords Interpretable Classifier, Linear Programming Relaxation”
Learning sparse two-level boolean rules (2016, methodology): “OOAx is similar to the multi-phase strategy [13] in using heuristic approaches to discover and select clauses before an optimization formulation is used to build a DNF with the selected clauses.”
Contrast Pattern-Based Classification for Bot Detection on Twitter (2019, methodology): “For building a contrast pattern-based classifier, there are three phases:Mining, Filtering, and Classification [23], [47].”
Optimal constraint-based decision tree induction from itemset lattices (2010, methodology): “Well-known algorithms include CBA (Liu et al. 1998), CMAR (Li et al. 2001) and CAEP (Dong et al. 1999); more general overviews can be found in (Knobbe et al. 2008; Bringmann et al. 2009).”
ENDER: a statistical framework for boosting decision rules (2010, background): “Induction of decision rules can be seen as an instance of the LeGo approach (Knobbe et al. 2008), a paradigm which relies on building global models by using local patterns.”