Learning Interpretable Classification Rules with Boolean Compressed Sensing

By Dmitry M. Malioutov, Kush R. Varshney, Amin Emad, Sanjeeb Dash. 2017

38 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Infectious Diseases, Safety, Robustness & Interpretability, Statistical Learning Theory

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Citations in the corpus

Local Rule-Based Explanations of Black Box Decision Systems (2018, background): “In the cases in which the training set is available, classification rules are also widely used to avoid building black boxes by directly designing a transparent classifier [17] which is locally or globally interpretable on its own [23, 25, 39].”
A Survey of Methods for Explaining Black Box Models (2018, methodology): “Finally, in [64] an approach f is designed to learn both sparse conjunctive and disjunctive clause rules from training data through a linear programming solution.”
Deep Neural Decision Trees (2018, methodology): “Some are model-agnostic (Ribeiro et al., 2016), while most are associated with a certain type of model, e.g., rule-based classifiers (Dash et al., 2015; Malioutov et al., 2017), nearest neighbour models (Kim et al., 2016), and neural networks (Kim et al., 2017).”
Learning Explainable Decision Rules via Maximum Satisfiability (2020, background): “A recent line of work, adapting ideas from Boolean compressed sensing, focuses on learning sparse k -cnf rules via linear programming, [9]–[11].”
Meaningful Explanations of Black Box AI Decision Systems (2019, methodology): “2018b) which is locally or globally interpretable on its own (Lakkaraju, Bach, and Leskovec 2016; Malioutov et al. 2017; Craven and Shavlik 1995).”
Group testing: an information theory perspective (2019, unknown): “In [69], [141], the authors show how such rules can be obtained using a relaxed noisy linear programming formulation of group testing (to be introduced in Section 3.”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “Malioutov et al. (2017) propose a rule-based classifier by applying a linear programming (LP) relaxation for interpretable sparse classification rules.”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Linear Programming Relaxation [285, 286] is a method for learning two-level Boolean rules in conjunctive normal form (AND-of-ORs) or disjunctive normal form (OR-of-ANDs) as a type of human-interpretable classification model.”
Economics, Fairness and Algorithmic Bias (2020, unknown): “Examples include point systems (Jung et al., 2017; Ustun and Rudin, 2016), additive models (Caruana et al., 2015; Lou et al., 2012, 2013) or checklists (Malioutov et al., 2017).”
Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018, methodology): “When the training set is available, decision rules are also widely used to proxy a black box model by directly designing a transparent classifier [21] which is locally or globally interpretable on its own [28, 32].”