Ensemble of a subset of kNN classifiers

Asma Gul (), Aris Perperoglou, Zardad Khan, Osama Mahmoud, Miftahuddin Miftahuddin, Werner Adler and Berthold Lausen ()
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Asma Gul: University of Essex
Aris Perperoglou: University of Essex
Zardad Khan: University of Essex
Osama Mahmoud: University of Essex
Miftahuddin Miftahuddin: University of Essex
Werner Adler: University of Erlangen-Nuremberg
Berthold Lausen: University of Essex

Advances in Data Analysis and Classification, 2018, vol. 12, issue 4, No 2, 827-840

Abstract: 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.

Keywords: Ensemble methods; Bagging; Nearest neighbour classifier; Non-informative features; 62H30; 68T05; 68T10 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-015-0227-5

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