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Ensemble of a subset of kNN classifiers

Advances in data analysis and classification, 2018, Vol.12 (4), p.827-840 [Peer Reviewed Journal]

The Author(s) 2016 ;Advances in Data Analysis and Classification is a copyright of Springer, (2016). All Rights Reserved. © 2016. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1862-5347 ;EISSN: 1862-5355 ;DOI: 10.1007/s11634-015-0227-5 ;PMID: 30931011

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Title:
Ensemble of a subset of kNN classifiers
Author: Gul, Asma ; Perperoglou, Aris ; Khan, Zardad ; Mahmoud, Osama ; Miftahuddin, Miftahuddin ; Adler, Werner ; Lausen, Berthold
Subjects: Chemistry and Earth Sciences ; Classification ; Classifiers ; Computer Science ; Computer simulation ; Data Mining and Knowledge Discovery ; Datasets ; Economics ; Finance ; Health Sciences ; Humanities ; Insurance ; Law ; Machine learning ; Management ; Mathematics and Statistics ; Medicine ; Physics ; Regular ; Regular Article ; Statistical Theory and Methods ; Statistics ; Statistics for Business ; Statistics for Engineering ; Statistics for Life Sciences ; Statistics for Social Sciences ; Support vector machines
Is Part Of: Advances in data analysis and classification, 2018, Vol.12 (4), p.827-840
Description: 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 k NN classifiers, ES k NN, 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 k NN, bagged k NN, random k NN, 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 k NN and its ensembles, and performs comparable to random forest and support vector machines.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 1862-5347
EISSN: 1862-5355
DOI: 10.1007/s11634-015-0227-5
PMID: 30931011
Source: ProQuest Central
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Ensemble of a subset of kNN classifiers

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