关键词: WilsonTh数据剪辑, 邻域粗糙集, 邻域互信息, 协同学习, 连续型数据

Abstract: A neighborhood rough sets based co-training classification model can deal with continuous data and utilize the unlabeled and labeled data to achieve better performance than the classifiers learning only from few labeled data. However, in the learning process, the unlabeled data may be wrongly labeled, which would introduce the noise to the training set and result in the instability of classification performance. This paper discusses the application of WilsonTh data editing in the neighborhood rough sets based co-training classification model. In the iteration, the classifiers label the unlabeled data with class symbol, use WilsonTh data editing to select right labeled data to the greatest extent possible, and add these labeled data to enlarge the training set for better quality of classifiers retraining. The experimental results on selected UCI datasets show that the application of WilsonTh data editing is more effective to improve the learning accuracy of neighborhood rough sets based co-training classification model.

Key words: WilsonTh data editing, neighborhood rough sets, neighborhood mutual information, co-training, continuous data