An Integer Optimization Approach to Associative Classification

By Allison Chang, Dimitris J. Bertsimas, Cynthia Rudin. 2012

22 citations recorded by the source corpus.

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Citations in the corpus

IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, methodology): “…objective functions and usage of combinatorial optimization techniques such as linear programming (LP) relaxations, sub-modular optimization, or Bayesian methods (Bertsimas, Chang, and Rudin 2012; Marchand and Shawe-Taylor 2002; Malioutov and Varsh-ney 2013; Boros et al. 2000; Wang et al. 2015) 1 .”
Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “However, recent work is revisiting the problem of interpretable learning through principled optimization formulations and achieving predictive performance approaching that of uninterpretable methods [40, 24, 18, 10, 34, 30, 55, 33, 50, 20].”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, methodology): “This hypothesis is also explored by [2], however, that method produces one list at a time, whereas our method produces the whole posterior of lists, and is more easily parallelized and adapted to larger scale problems.”
IPBoost - Non-Convex Boosting via Integer Programming (2020, methodology): “…in particular, has been used successfully to incorporate combinatorial structure in classification, see, e.g., (Bertsimas & Shioda, 2007; Chang et al., 2012; Bertsimas & King, 2015; Bertsimas et al., 2016), as well as, (Günlük et al., 2016; Bertsimas & Dunn, 2017; Dash et al., 2018;…”
Exact Rule Learning via Boolean Compressed Sensing (2013, methodology): “Bertsimas et al. (2012); Letham et al.”