Performance of classification models from a user perspective

By David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens. 2011

131 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems, Evaluation & Benchmarks, Human-Computer Interaction

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Citations in the corpus

Learning Credible Models (2017, methodology): “…w ∗ | θ | [ i ] is the i th largest parameter sorted by magnitude; and ⊙ is the training time, Sun et al . explore using features identified as relevant Vapnik et al . explore the theory of learning with privileged information In the linear setting, the method simplifies to l 2 regularization on”
Falling Rule Lists (2014, background): “…context dependent (see Kodratoﬀ, 1994; Pazzani, 2000; Freitas, 2014; Huys-mans et al. , 2011; Allahyari & Lavesson, 2011; Martens & Baesens, 2010; R¨uping, 2006; Verbeke et al. , 2011; Martens et al. , 2011), and no matter how one measures it in one domain, it can be diﬀerent in the next domain.”
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].”
A data-driven approach to predict the success of bank telemarketing (2014, background): “AUC is a popular classificationmetric [21] that presents advantages of being independent of the class frequency or specific false positive/negative costs.”
Causal Falling Rule Lists (2015, background): “Interpretability is highly con- text dependent (Kodratoff 1994; Pazzani 2000; Martens et al. 2011); no matter how one measures it in one domain, it can be different in the next domain.”
CBC: An associative classifier with a small number of rules (2014, background): “The comprehensibility of a classifier is vital in decision support systems [18, 12].”
Interpretability of machine learning models and representations: an introduction (2016, background): “Other authors use the term interpretability as strict synonym of understandability [5, 10] or comprehensibility [3, 6, 8, 13].”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, background): “Both can be adjusted to suit a domain-specific notion of interpretability; it is well-known that interpretability comes in different forms for different domains (Martens and Baesens, 2010; Martens et al., 2011; Huysmans et al., 2011; Allahyari and Lavesson, 2011; Rüping, 2006; Freitas, 2014).”
Comprehensibility of Classification Trees–Survey Design (2019, background): “The influence of visualization on comprehensibility has been stressed [16] but empirical studies are missing, therefore the suggested survey also considers visualization factors.”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, background): “Therefore, depending on the setting other aspects beside discriminatory power have to be taken into account when selecting a classification technique, such as for instance comprehensibility or operational efficiency (Martens et al., 2011).”
A Survey on the Explainability of Supervised Machine Learning (2020, background): “Furthermore, Martens et al. (2011) distinguish between the phases in the data mining process that incorporate the monotonicity constraint into the system.”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “The fifth cluster contains reviews that analysed the methods for explainability for either regression [47] or classification [31, 48, 49] problems.”