| Summary: | 碩士 === 國立交通大學 === 科技管理研究所 === 107 === Rotation forest algorithm has been developed for more than ten years. Many scholars have successively proposed improved versions, and in some fields, there have been application results. However, compared with other algorithms such as random forests, the disadvantage is that the calculation is complicated and time-consuming, the improvement of precision is quite limited as well.
This research work proposes a classifier ensemble method combining rotation forest and AdaBoost, which is called Random Rotboost. In the processing of training data, the data diversity is increased by randomly extracting feature sets, and then combined with rotation forest and AdaBoost to form an ensemble classifier. In the experimental section, this paper conducted experiments on ten data sets, and compared the other four ensemble algorithms as a control group to compare with Random Rotboost. The results show that Random Rotboost can maintain high precision when the execution time is the same or even less than the other algorithms.
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