Bayesian Rule Sets for Interpretable Classification

By Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille. 2016

62 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, methodology): “…kinds of logic-based methods [Wang and Rudin, 2015, Lakkaraju et al., 2016, Singh et al., 2016, Liu and Tsang, 2016, Safavian and Landgrebe, 1991, Wang et al., 2017], techniques for extracting local explanations from black-box models [Ribeiro et al., 2016, Lei et al., 2016, Adler et al., 2016,…”
A Nutritional Label for Rankings (2018, background): “With the exception of recent machine learning results that enable interpretability of particular classes of algorithms [6, 7, 12], recent scholarship on algorithmic accountability has primarily focused on enabling an analyst to retroactively verify particular properties rather than proactively expos”
Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models (2018, methodology): “Implementation We use R and python packages [10, 24, 26] for baseline methods except for BRS which has the code publicly available2.”
A Semiparametric Approach to Interpretable Machine Learning (2020, background): “For instance, [42] state that rule sets are interpretable in a certain sense, and propose a procedure for learning rule sets for the purpose of classification.”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, background): “A shorter version of this work appeared at the International Conference on Data Mining (Wang et al., 2016).”
Multi-Value Rule Sets (2017, methodology): “[22]).”
Semantic Explanations of Predictions (2018, background): “(Wang et al. 2016) exploit the characteristics of classiﬁcation models by exploiting and relaxing their decision boundary to approximate explanations.”
On Explaining Decision Trees (2020, unknown): “[2,68,25,32,1,7,57,24,26,70,67,58,44,30,17,5,73,35,4,78,69,10,34,72,71,36,46,11,45].”
Explainable Sentiment Analysis with Applications in Medicine (2018, methodology): “A simpler way to evaluate interpretability is via a quantifiable proxy [42]: the interpretable algorithm is presented as an optimization of a class models that already are claimed to be interpretable [46].”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “…al. (2016) FRL Classification Rule-based Wang and Rudin (2015) IDS Classification Rule-based Lakkaraju et al. (2017) BRS Classification Rule-based Wang et al. (2016) OT-SpAM Classification Tree-based Wang et al. (2015a) Tree Regularization Classification Tree-based Wu et al. (2018) NDT…”