关键词: 旋转森林, 分类器集成, 主成分分析, 决策树

Abstract: In order to improve accuracy of decision tree, the rotation forest classifier ensemble algorithm based on feature transformation is introduced. To enhance the?diversity of?base?classifiers and to improve?predicting?accuracy, the feature set is split into some subsets and principal component analysis is applied to each subset to create new subsample data for each base classifier. Using Weka, rotation forest is compared with Bagging and AdaBoost. Pruned and unpruned J48 decision tree are applied to be base classifier respectively and the average accuracy of 10 times10 fold cross-validation is to be comparison basis. The result shows that the accuracy of rotation forest is higher than others, and it proves that rotation forest is an effective classifier ensemble algorithm of decision tree.

Key words: rotation forest, classifier ensemble, principal components analysis, decision tree