Random relevant and non-redundant feature subspaces for co-training
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Co-training with relevant random subspaces
Neurocomputing
DCPE co-training for classification
Neurocomputing
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For many data mining applications, it is necessary to develop algorithms that use unlabeled data to improve the accuracy of the supervised learning. Co-Training is a popular semi-supervised learning algorithm. It assumes that each example is represented by two or more redundantly sufficient sets of features (views) and these views are independent given the class. However, these assumptions are not satisfied in many real-world application domains. Therefore, we present a framework called Co-Training by Committee (CoBC), in which a set of diverse classifiers are used to learn each other. The framework is a simple, general single-view semi-supervised learner that can use any ensemble learner to build diverse committees. Experimental studies on CoBC using Bagging, AdaBoost and the Random Subspace Method (RSM) as ensemble learners demonstrate that error diversity among classifiers leads to an effective Co-Training that requires neither redundant and independent views nor different learning algorithms.