Nearest Neighbour Classification with Monotonicity Constraints

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Data preprocessing techniques for classification without discrimination (2011, neutral): “One noteworthy exception is monotone classification [10,18].”
Three naive Bayes approaches for discrimination-free classification (2010, background): “One noteworthy exception is monotonic classification (Kotlowski et al. 2007; Duivesteijn and Feelders 2008), where the aim is to find a classification that is monotone in a given attribute.”
Monotonic Calibrated Interpolated Look-Up Tables (2015, background): “Duivesteijn and Feelders (2008) proposed re-labeling samples before applying nearest neighbors based on a monotonicity violation graph with the training examples at the vertices.”
Comprehensible classification models: a position paper (2014, methodology): “In addition, in [18] the training data is first relabeled to make it monotone, and then a nearest-neighbor algorithm is modified to predict class labels in a way that respects the monotonicity constraints.”
Discrimination Aware Decision Tree Learning (2010, unknown): “One noteworthy exception is monotonic classification [11, 5], where the aim is to find a classification that is monotone in a given attribute.”
Discrimination-aware classification (2011, unknown): “One noteworthy exception is monotone classification [30, 53].”