Building Acceptable Classification Models

By David Martens, Bart Baesens. 2010

28 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

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).”
Falling Rule Lists (2014, background): “…is very 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…”
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].”
Methods and Models for Interpretable Linear Classification (2014, background): “2 Useful Interpretability Sets The process of building interpretable models often requires an approach that can train models that satisfy different hard constraints [64, 66].”
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).”
Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, background): “…integration that is not explicitly incorporated by the three previous studies, is the monotonicity constraint (Ben-David, 1995; Sill, 1998; Feelders and Pardoel, 2003; Velikova and Daniels, 2004; Altendorf et al , 2005; Martens et al , 2006; Van Gestel et al , 2007; Martens and Baesens, 2008).”