Ensemble of a subset of kNN classifiers

Asma Gul, Aris Perperoglou, Zardad Khan, Osama Mahmoud, Miftahuddin Miftahuddin, Werner Adler, Berthold Lausen*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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.

Original languageEnglish
Pages (from-to)827-840
Number of pages14
JournalAdvances in Data Analysis and Classification
Volume12
Issue number4
Early online date22 Jan 2016
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Bagging
  • Ensemble methods
  • Nearest neighbour classifier
  • Non-informative features

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