Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms

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Learning Credible Models (2017, background): “This restriction could be probabilistic [16] or monotone in certain arguments identified by experts [2, 23, 33].”
Building Acceptable Classification Models (2010, background): “Although almost all research into this field is focused on the monotonicity constraint [2, 7, 21, 40, 47, 61], the taxonomy of possible domain constraints, shown in Table 3.”
Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, background): “…aspects 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 ,…”
Learning from Sparse Data by Exploiting Monotonicity Constraints (2005, background): “Others have shown that these qualitative influences could be usefully incorporated into learning algorithms, including the CN2 learning system, decision tree algorithms, and the back-propagation neural network algorithm [7, 2, 3 , 9, 11, 8, 20, 15].”
Nearest Neighbour Classification with Monotonicity Constraints (2008, background): “Examples are: classification trees [7,12,22], neural networks [3,26], and Bayesian networks [1,13].”