Solving Partially Monotone Problems with Neural Networks

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Learning Credible Models (2017, background): “Other attempts to enforce monotonicity in nonlinear models [1, 26, 32] aim to increase performance.”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “…classiﬁcation techniques have been put forward to deal with monotonicity, such as for Bayesian learning [1], classiﬁcation trees [7,21,24], classiﬁcation rules [43] and neural networks [54,66]; e.g., in the medical diagnosis [47], house price prediction [65] and credit scoring [21, 54] domains.”
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, methodology): “This is for example the case for Bayesian network learning algorithms (Altendorf et al, 2005), AntMiner+ (Martens et al , 2006) and neural networks (Velikova et al , 2006).”