Fuzzy Monotonic K-Nearest Neighbor Versus Monotonic Fuzzy K-Nearest Neighbor

In real-life applications, monotonic classification is a widespread task, where the improvement of a particular input value cannot result in an inferior output. A common drawback of the existing algorithms for monotonic classification is their sensitivity to noise data which particularly refer to monotonicity violations in the monotonic circumstance. Motivated by weakening the impact of noises, the fuzzy monotonic K-nearest neighbor (FMKNN) is proposed in this article, which constructs monotonic classifiers by taking advantage of the fuzzy dominance relation between a pair of instances, especially that between incomparable instances for the first time. Through tuning the thresholds of fuzzy dominance relation degrees, FMKNN intends to decrease the disturbance caused by noises which considerably affect the selection range of the K-nearest neighbors in different extent. The experimental results show that the best average improvement degrees of FMKNN in terms of the KNN-based and non-KNN-based classifiers on all the involved datasets arrive at 28%, 11%, and 29% with respect to ACCU, MAE, and NMI, respectively, which demonstrates the superiority of our proposed FMKNN over other state-of-the-art monotonic classifiers including the monotonic fuzzy K-nearest neighbor (MFKNN) which disperses the impact of noise data by converting crisp class labels into class membership vectors.