利用旋转森林变换的异构多分类器集成算法

为了增强集成系统中各分类器之间的差异性，提出了一种使用旋转森林策略集成两种不同模型分类器的方法，即异构多分类器集成学习算法．首先采用旋转森林对原始样本集进行变换划分，获得新的样本集;然后通过特定比例选择分类精度高的支撑矢量机或分类速度较快的核匹配追踪作为基本的集成个体分类器，并对新样本集进行分类，获得其预测标记；最后结合两种模型下的预测标记．该算法通过结合两种不同分类器模型，实现了精度和速度互补，将二者混合集成后改善了集成系统泛化误差，相比单个模型集成提高了系统分类性能．对UCI数据集和遥感图像数据集的仿真实验结果表明，文中算法相比单一分类器集成缩短了运行时间，同时提高了系统的分类准确率．

Isomerous multiple classifier ensemble via transformation of the rotating forest

In order to boost the diversity among individual classifiers of an ensemble, a new ensemble method is proposed that combines two different classifier models via a transformation of rotation forest, named by isomerous multiple classifier ensemble．Firstly, the original samples are transformed and divided by the rotating forest to obtain new samples．Then support vector machine with the high accuracy of classification or kernel matching pursuit with the speedy classification is selected as a basic classifier model based on a special proportion, the selected classifier is used to classify the new samples, and the predictive labels are obtained. Finally, the predictive labels given by two different models are combined to obtain the final predictive labels of an ensemble. Particularly, the proposed method achieves the complementarity of accuracy and speed by combining two different classifier models, and it is important that isomerous classifier ensemble improve the generalization error of an ensemble and increases the classification performance. According to the experimental results of classification for UCI datasets and remote sensing image datasets, it is illustrated that the proposed method shortens obviously the running time and improves the accuracy of classification, compared with an ensemble based on the single classifier model.