Particle swarm optimisation-based KNN for improving KNN and ensemble classification performance Online publication date: Wed, 11-Nov-2015
by Debojit Boro; Dhruba K. Bhattacharyya
International Journal of Innovative Computing and Applications (IJICA), Vol. 6, No. 3/4, 2015
Abstract: In this paper we use KNN algorithm for our ensemble classification process that finds out the nearest K training samples given a test sample where each sample is predictions vector generated by combined computation of classifiers ensemble and algebraic combiners. In this attempt to reduce the computational time involved in finding K training samples by KNN, we used particle swarm optimisation (PSO) with KNN which randomly selects the training samples from the training set until a global consensus is reached among the particles and label that test sample with an appropriate class by weighted majority voting (WMV) of K training samples. The proposed method demonstrated better ensemble performance as compared to KNN and other traditional ensemble methods in terms of computational time and generalisation accuracies when tested over several datasets from UCI repository and other high dimensional datasets.
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