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Ensemble of subset of k-nearest neighbours models for class membership probability estimation

Gul, A and Khan, Z and Perperoglou, A and Mahmoud, O and Miftahuddin, M and Adler, W and Lausen, B (2016) Ensemble of subset of k-nearest neighbours models for class membership probability estimation. In: UNSPECIFIED, ? - ?.

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Abstract

© Springer International Publishing Switzerland 2016. Combining multiple classifiers can give substantial improvement in prediction performance of learning algorithms especially in the presence of noninformative features in the data sets. This technique can also be used for estimating class membership probabilities. We propose an ensemble of k-Nearest Neighbours (kNN) classifiers for class membership probability estimation in the presence of non-informative features in the data. This is done in two steps. Firstly, we select classifiers based upon their individual performance from a set of base kNN models, each generated on a bootstrap sample using a random feature set from the feature space of training data. Secondly, a step wise selection is used on the selected learners, and those models are added to the ensemble that maximize its predictive performance. We use bench mark data sets with some added non-informative features for the evaluation of our method. Experimental comparison of the proposed method with usual kNN, bagged kNN, random kNN and random forest shows that it leads to high predictive performance in terms of minimum Brier score on most of the data sets. The results are also verified by simulation studies.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Studies in Classification, Data Analysis, and Knowledge Organization
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 05 Dec 2016 21:08
Last Modified: 23 Jan 2019 05:16
URI: http://repository.essex.ac.uk/id/eprint/18345

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