earticle

영어

In recent years, semi-supervised learning has been a hot research topic in machine learn-ing area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each exam-ple is represented by two or more redundantly sufficient sets of features (views) and addi-tionally these views are independent given the class. To improve the performance and ap-plicability of co-training, ensemble learning, such as bagging and random subspace has been used along with co-training. In this work, we propose to use the rough set based ensem-ble learning method with co-training algorithm (RSCO). Inherited the inherent characteris-tics of rough set, ensemble learning is expected to meet both the diversity and accuracy re-quirement. Finally experimental results on the UCI data sets demonstrate the promising per-formance of RSCO.