A Meta-Ensemble Classifier Approach: Random Rotation Forest

Year 2019, Volume: 7 Issue: 2, 182 - 187, 30.04.2019

Erdal Taşcı

https://doi.org/10.17694/bajece.502156

Cited By: 7

Abstract

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 so 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 and recall values. Our method can be used for
image/pattern recognition and machine learning problems.

Keywords

Ensemble learning, Machine learning, Pattern recognition, Data mining, Classification, Rotation forest

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