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Automated Classifier Combination for Pattern Recognition
Authors: Alper Baykut, Aytül Erçil
Published in: Proceedings of MCCS'2003
Publication year: 2003
Abstract: This study covers weighted combination methodologies for multiple
classifiers to improve classification accuracy. The classifiers are extended to
produce class probability estimates besides their class label assignments to be
able to combine them more efficiently. The leave-one-out training method is
used and the results are combined using proposed weighted combination
algorithms. The weights of the classifiers for the weighted classifier
combination are determined based on the performance of the classifiers on the
training phase. The classifiers and combination algorithms are evaluated using
classical and proposed performance measures. It is found that the integration of
the proposed reliability measure, improves the performance of classification. A
sensitivity analysis shows that the proposed polynomial weight assignment
applied with probability based combination is robust to choose classifiers for
the classifier set and indicates a typical one to three percent consistent
improvement compared to a single best classifier of the same set.

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