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Prototype selection for dynamic classifier and ensemble selection

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Abstract

In dynamic ensemble selection (DES) techniques, only the most competent classifiers, for the classification of a specific test sample, are selected to predict the sample’s class labels. The key in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers’ competence is usually estimated according to a given criterion, which is computed over the neighborhood of the test sample defined on the validation data, called the region of competence. A problem arises when there is a high degree of noise in the validation data, causing the samples belonging to the region of competence to not represent the query sample. In such cases, the dynamic selection technique might select the base classifier that overfitted the local region rather than the one with the best generalization performance. In this paper, we propose two modifications in order to improve the generalization performance of any DES technique. First, a prototype selection technique is applied over the validation data to reduce the amount of overlap between the classes, producing smoother decision borders. During generalization, a local adaptive K-Nearest Neighbor algorithm is used to minimize the influence of noisy samples in the region of competence. Thus, DES techniques can better estimate the classifiers’ competence. Experiments are conducted using 10 state-of-the-art DES techniques over 30 classification problems. The results demonstrate that the proposed scheme significantly improves the classification accuracy of dynamic selection techniques.

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Notes

  1. The term base classifier refers to a single classifier belonging to an ensemble or a pool of classifiers.

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Acknowledgments

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the École de technologie supérieure (ÉTS Montréal) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).

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Authors and Affiliations

  1. Département de génie de la production automatisée, École de Technologie Supérieure (ETS), Montreal, QC, Canada

    Rafael M. O. Cruz & Robert Sabourin

  2. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

    George D. C. Cavalcanti

Authors
  1. Rafael M. O. Cruz
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  2. Robert Sabourin
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  3. George D. C. Cavalcanti
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Correspondence to Rafael M. O. Cruz.

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Cruz, R.M.O., Sabourin, R. & Cavalcanti, G.D.C. Prototype selection for dynamic classifier and ensemble selection. Neural Comput & Applic 29, 447–457 (2018). https://doi.org/10.1007/s00521-016-2458-6

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