An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

By Johan Huysmans, Karel Dejaeger, C. Mues, Jan Vanthienen, Bart Baesens. 2011

448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

In Safety, Robustness & Interpretability

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, methodology): “Informativeness [5, 44, 21, 25, 26, 45, 30, 32, 34, 35, 37, 38, 41, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 86, 87, 88, 89, 59, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 1”
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “making and troubleshooting models, which are the main two reasons one might require an interpretable or explainable model. Interpretability is a domain-speciﬁc notion [Freitas, 2014, Kodratoff, 1994, Huysmans et al., 2011, Rüping, 2006], so there cannot be an all-purpose deﬁnition. Usually, however,”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Interpretability is a domain-speciﬁc notion [Freitas, 2014, Kodratoff, 1994, Huysmans et al., 2011, Rüping, 2006], so there cannot be an all-purpose deﬁnition.”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “[149] investigated decision trees, decision tables, propositional rules, and oblique rules in order to understand which is the most interpretable.”
A Survey of Methods for Explaining Black Box Models (2018, background): “However, other works focus on particular aspects like the impact of representation formats on comprehensibility [43] or the interpretability issues in term of advantages and disadvantages of selected predictive models [32].”
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance (2016, background): “Experiments usually involve showing humans these visualizations, and measuring human precision when predicting the model’s behavior on random instances, and the time (effort) required to make those predictions [7, 8, 9].”
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, neutral): “…related concepts of explanation and comprehensibility in statistical modeling have been explored in many past works [Bratko (1997), Madigan, Mosurski and Almond (1997), Giraud-Carrier (1998), Rüping (2006), Huysmans et al. (2011), Vellido, Mart́ın-Guerrero and Lisboa (2012), Freitas (2014), e.g.].”
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, neutral): “(This approach is similar to those used in prior work to measure comprehensibility [20].”
Interpretable classification models for recidivism prediction (2015, background): “n the need for a nomogram (which is a visualization tool for more difﬁcult calculations). Reviews and commentary on interpretability in machine learning include works of Holte (1993); Ruping¨ (2006); Huysmans et al. (2011); Freitas (2014); Vellido et al. (2012); Plate (1999). Interpretability can be”
Deep Weighted Averaging Classifiers (2018, background): “As emphasized by papers that have made use of user studies [16, 22, 36], careful empirical work is required to evaluate the effectiveness of explanations, and we leave such an evaluation of this approach for future work.”
Manipulating and Measuring Model Interpretability (2018, background): “[45] Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens.”
An optimization approach to learning falling rule lists (2017, neutral): “In addition, our work also relates to those that underline the importance of the interpretability of models (Freitas, 2014; Huysmans et al., 2011; Kodratoff, 1994; Martens and Baesens, 2010).”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, background): “Both models have priors that can be adjusted to suit a domain-speciﬁc notion of interpretability, as it is well-known that interpretability comes in different forms for different domains [3, 15, 21, 29, 30, 39].”