An equilibrium optimizer-based parameter independent fuzzy kNN classifier for classification of medical datasets

The kNN classifier is the most popular, supervised machine-learning technique, but the main disadvantage of this algorithm is that it has restricted access to the class distributions in a training point set and treats all the instances equally. In kNN classification, fuzzy sets are used to obtain the membership degrees of each point to the classes known as fuzzy kNN (FkNN) classification. Although the FkNN classifier enhances the performance of the kNN, it does not consider the effect of noisy and redundant instances, which makes it ineffective. Moreover, the performance of kNN is dependent on the value of k (number of nearest neighbours). Considering these issues, we present a novel algorithm that simultaneously tunes the class-dependent feature weights and k value using an effective meta-heuristic algorithm, the Enhanced Equilibrium Optimization technique. Several experiments have been conducted on different biomedical datasets, and the proposed approach has outperformed the other standard classifiers in terms of accuracy.