Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees

Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this problem by using unlabeled data together with labeled data to improve the performance. Co-Training is a popular semi-supervised learning algorithm that has the assumptions that each example is represented by two or more redundantly sufficient sets of features (views) and additionally these views are independent given the class. However, these assumptions are not satisfied in many real-world application domains. In this paper, a framework called Co-Training by Committee (CoBC) is proposed, in which an ensemble of diverse classifiers is used for semi-supervised learning that requires neither redundant and independent views nor different base learning algorithms. The framework is a general single-view semi-supervised learner that can be applied on any ensemble learner to build dive...

• Type: Journal
• Year: 2008
• Where: IJSI
• Authors: Mohamed Farouk Abdel Hady, Friedhelm Schwenker
• Keywords: Co-training, Data Mining, IJSI 2008, Semi-supervised Learning