Co-training with adaptive Bayesian classifier combination

In a classification problem, when there are multiple feature views and unlabeled examples, co-training can be used to train two separate classifiers, label the unlabeled data points iteratively and then combine the resulting classifiers. Especially when the number of labeled examples is small due to expense or difficulty of obtaining labels, co-training can improve classifier performance. For binary classification problems, mostly, the product rule has been used to combine classifier outputs. In this paper, we propose an adaptive Bayesian classifier combination method which selects either the Bayesian or the product combination method based on the belief values. We compare our adaptive Bayesian method with Bayesian, product and maximum classifier combination methods for the multi-class pollen image classification problem. Two different feature sets, Haralick's texture features and features obtained using local linear transforms are used for co-training. Experimental results show that adaptive Bayesian combination with co-training performs better than the other three methods.