Abstract:
This thesis studies methodologies to combine multiple classifiers to improve classification accuracy. Different classifiers, training methods and combination algorithms are covered throughout this study. The classifiers are extended to produce class probability estimates besides their class assignments to be able to combine them more efficiently. They are integrated in a framework to provide a toolbox for classifier combination. 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 per cent consistent improvement compared to a single best classifier of the same set.
