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A new rotation forest ensemble algorithm
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DOI：10.1007/s13042-022-01613-8.png)
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Abstract
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Random forest, a popular ensemble approach in machine learning, has received much attention of researchers in different fields due to its excellent performance. Especially, in the study of classification, it is often used as an effective classifier. Considering that the accuracy and diversity of each base classifier are two main factors that affect the performance of random forest, this paper proposes a new rotation forest ensemble method to increase the diversity of each tree in the forest, which is based on feature extension and transformation. Also, a weighting vote for base classifiers is applied to integrate the final ensemble results instead of to average the accuracy of ensemble learners. In order to illustrate the effectiveness of the proposed algorithm, the experiments conduct with thirty benchmark classification datasets available from the UCI repository and two face recognition databases. Experimental results demonstrate that the proposed algorithm can achieve higher classification accuracy in most cases compared to the other ensemble classifiers.
Keywords:
Random forest
Ensemble learning
Decision tree
Discriminative locality alignment
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