Local Classifier Weighting by Quadratic Programming

ÇEVİKALP H., Polikar R.

IEEE TRANSACTIONS ON NEURAL NETWORKS, vol.19, no.10, pp.1832-1840, 2008 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 10
  • Publication Date: 2008
  • Doi Number: 10.1109/tnn.2008.2005301
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1832-1840
  • Keywords: Classification, classifier fusion, combining multiple classifiers, ensemble learning, local classification accuracy, neural networks, quadratic programming, SUPPORT VECTOR MACHINES, COMBINING CLASSIFIERS, MULTIPLE CLASSIFIERS, NEURAL-NETWORKS, FUSION, COMBINATION, SYSTEMS
  • Eskisehir Osmangazi University Affiliated: Yes


It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures-many of which are heuristic in nature-have been developed for this goal. In this brief, we describe a dynamic approach to combine classifiers that have expertise in different regions of the input space. To this end, we use local classifier accuracy estimates to weight classifier outputs. Specifically, we estimate local recognition accuracies of classifiers near a query sample by utilizing its nearest neighbors, and then use these estimates to find the best weights of classifiers to label the query. The problem is formulated as a convex quadratic optimization problem, which returns optimal nonnegative classifier weights with respect to the chosen objective function, and the weights ensure that locally most accurate classifiers are weighted more heavily for labeling the query sample. Experimental results on several data sets indicate that the proposed weighting scheme outperforms other popular classifier combination schemes, particularly on problems with complex decision boundaries. Hence, the results indicate that local classification-accuracy-based combination techniques are well suited for decision making when the classifiers are trained by focusing on different regions of the input space.