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Experiments on Combining Classifiers

In this paper, experiments on various classifiers and combining these classifiers are done, reported and analyzed. Combining the classifiers means having the single classifiers support each other in making a decision, instead of having only a single classifier’s decision as the final decision. The base experiment involves, both, applying different single classifiers on a dataset and applying the combination of those classifiers on the same dataset. Also, the same experiment is conducted after applying feature reduction, Bagging and Boosting methods separately. The dataset used in our experiments has not been used in any publication yet. The performance of the classifiers in terms of accuracies is compared in all experiments. In the experiments, classifier combinations perform slightly better than the single classifiers in terms of overall accuracy. It is also seen that bad classifiers and features may contain valuable information for performance improvement in terms of accuracy by combining rules.