Ensemble based co-training
Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh · Data Archiving and Networked Services (DANS) · 2011
Recently Semi-Supervised learning algorithms such as co-training are used in many application domains. In co-training, two classifiers based on different views of data or on different learning algorithms are trained in parallel and then unlabeled data that are classified differently by the classifiers but for which one classifier has large confidence are labeled and used as training data for the other. In this paper, a new form of co-training, called Ensemble-Co-Training, is proposed that uses an ensemble of different learning algorithms. Based on a theorem by Angluin and Laird that produces an approximately correct identification with high probability for reliable examples, we propose a criterion for finding a subset of high-confidence predictions and error rate for a classifier in each iteration of the training process. Experiments show that the new method in almost all domains gives better results than the other methods.