Monotonicity maintenance in information-theoretic machine learning algorithms

By Arie Ben-David. 2004

96 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

An optimization approach to learning falling rule lists (2017, background): “There are some works on decision trees with monotonicity constraints (e.g. Altendorf et al., 2005; Ben-David, 1995; Feelders and Pardoel, 2003), but they focus mostly on enforcing the monotonic relationship between certain attributes and ordinal class labels.”
Falling Rule Lists (2014, methodology): “Because the monotonicity properties we enforce are much stronger than those of Ben-David (1995); Feelders & Pardoel (2003); Altendorf et al. (2012) (we are looking at monotonicity along the whole list rather than for individual features), we do ﬁnd that accuracy is sometimes sacriﬁced, but not…”
Methods and Models for Interpretable Linear Classification (2014, background): “In turn, recent approaches to interpretable predictive modeling have sought to produce models with monotonicity constraints so that the relationship between input variables and the predicted response value matches the views of domain experts [10, 87, 114, 77].”
Monotonic Calibrated Interpolated Look-Up Tables (2015, background): “…net constrain function yes 50 Wang (1994) neural net constrain function yes 150 Mukarjee and Stern (1994) kernel estimate post-process yes 2447 Ben-David (1995) tree penalize splits yes 125 Sill and Abu-Mostafa (1997) neural net penalize pairs no 550 Sill (1998) neural net constrain function…”
Solving Partially Monotone Problems with Neural Networks (2007, background): “Several data mining techniques have been developed, which incorporate monotonicity constraints such as neural networks ([3],[5],[11],[12]), rational cubic interpolation of onedimensional functions ([10]), decision trees ([1],[8]), etc.”
Pruning for Monotone Classification Trees (2003, background): “Ben-David [ BD95 ] was the first to incorporate the monotonicity constraint in classification tree construction.”
Comparison of classification accuracy using Cohen's Weighted Kappa (2008, methodology): “classification problems are monotonic (Ganzach, 1993; Ben-David, 1995; Cao-Van & De Baetes, 2002). This property implies that the further the disagreement counts are from the main diagonal of a confusion matrix – the severity of the disagreement increases and vice versa. Consequently, one expects th”
Rule learning with monotonicity constraints (2009, methodology): “…Rough Set Approach (DRSA) (Greco et al., 2001) along with decision rule induction (Dembczy´nski et al., 2008), monotone classi.cation trees (Ben-David, 1995; Potharst & Feelders, 2002), monotone networks (Sill, 1998), instance-based methods (Ben-David et al., 1989; Cao-Van &…”
Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, background): “…related to domain knowledge 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 ,…”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “…constraint has not been addressed in the SVM rule extraction literature, although the application of rule induction techniques that do obtain this feature, such as AntMiner+ [43] and tree inducers proposed in [7,24], as SVM rule extraction techniques is a ﬁrst step into that direction.”