  This paper proposes a new model based on Fuzzy k-Nearest Neighbors for
classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).
Real-life data-sets often do not comply with monotonic constraints due to class
noise. MonFkNN incorporates a new calculation of fuzzy memberships, which
increases robustness against monotonic noise without the need for relabeling.
Our proposal has been designed to be adaptable to the different needs of the
problem being tackled. In several experimental studies, we show significant
improvements in accuracy while matching the best degree of monotonicity
obtained by comparable methods. We also show that MonFkNN empirically achieves
improved performance compared with Monotonic k-NN in the presence of large
amounts of class noise.
