A Meta-Ensemble Classifier Approach: Random Rotation Forest

Ensemble learning is a popular and intensively studied field in machine learning and pattern recognition to increase the performance of the classification. Random forest is very important for giving fast and effective results. On the other hand, Rotation Forest can get better performance than Random Forest. In this study, we present a meta-ensemble classifier, called Random Rotation Forest to utilize and combine the advantages of two classifiers (e.g. Rotation Forest and Random Forest). In the experimental studies, we use three base learners (namely, J48, REPTree, and Random Forest) and two meta-learners (namely, Bagging and Rotation Forest) for ensemble classification on five datasets in UCI Machine Learning Repository. The experimental results indicate that Random Rotation Forest gives promising results according to base learners and bagging ensemble approaches in terms of accuracy rates, AUC, precision, recall, and F-measure values. Our method can be used for image/pattern recognition and machine learning problems.

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