Ensemble of a subset of kNN classifiers

Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O. ORCID: https://orcid.org/0000-0003-0342-6704, Miftahuddin, M., Adler, W. and Lausen, B. (2018) Ensemble of a subset of kNN classifiers. Advances in Data Analysis and Classification. pp. 827-840. ISSN 1862-5355

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Abstract

Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines.

Item Type:	Article
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Science > School of Biological Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	27 Oct 2026 10:08
Last Modified:	30 Oct 2026 00:08
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104862
DOI:	10.1007/s11634-015-0227-5

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