Integrating Classification and Association Rule Mining

By B. Liu, Wynne Hsu, Yiming Ma. 1998

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In Statistical Learning Theory

Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “Associative classification methods assemble decision lists or decision sets from a set of pre-mined rules, generally either by greedily adding rules to the model one by one, or simply including all “top-scoring” rules into a decision set, where each rule is scored separately according to a scoring f”
Interpretable Two-level Boolean Rule Learning for Classification (2015, methodology): “The second formulation replaces the 0-1 classiﬁcation error cost with the Hamming distance from the current rule to the closest rule that correctly classiﬁes a sample; we propose block coordinate descent and alternating minimization approaches for optimizing the objective in the second formulation.”
Interpreting tree ensembles with inTrees (2014, methodology): “[11] use feature selection to select a set of relevant and non-redundant conditions from associative classification rules [27].”
Learning Certifiably Optimal Rule Lists (2017, background): “Similarly, there are decision list and associative classification methods that construct rule lists iteratively in a greedy way (Rivest, 1987; Liu et al., 1998; Li et al., 2001; Yin and Han, 2003; Sokolova et al., 2003; Marchand and Sokolova, 2005; Vanhoof and Depaire, 2010; Rudin et al., 2013).”
Learning Fair Rule Lists (2019, methodology): “CORELS [4] is a classification [45] algorithm that enables to learn provably optimal rule lists.”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “, [4,33,39,40,42,55]), inductive logic programming approaches (e.”
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, ”
An optimization approach to learning falling rule lists (2017, methodology): “the RIPPERk algorithm (Cohen, 1995) and the CBA algorithm (Liu et al., 1998); see Thabtah (2007) for a comprehensive review) and inductive logic programming (Muggleton and De Raedt, 1994).”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, methodology): “On the other hand, interpretable decision sets are smaller than decision lists learned by other stateof-the-art methods [25, 24] and just as accurate, because they are learned via a joint optimization of accuracy and interpretability.”
Scalable Bayesian Rule Lists (2016, methodology): “For convenience, we use the LUCS-KDD java implementations of TCV(10 cross validation) for both CBA and CMAR.”
CPAR: Classification based on Predictive Association Rules (2003, methodology): “As in [7] and [6], 26 datasets from UCI Machine Learning Repository are used.”
Mining Optimal Class Association Rule Set (2001, methodology): “Mining association rules [2] is a central task of data mining and has shown applications in various areas [1,3,4].”
Cluster-grouping: from subgroup discovery to clustering (2004, methodology): “Associative classification techniques such as CBA (Liu et al. 1998) and CMAR (Li et al.”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “The idea of frequent pattern-based classification has been exploited by previous studies in different domains, including: (1) associative classification [14, 13, 25, 6, 19], where association rules are generated and analyzed for classification; and (2) graph classification [7], text categorization [”