Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction.