A hybrid generative/discriminative method for semi-supervised classification
Training methods for machine learning are often characterized as being generative or discriminative. We present a new co-training style algorithm which employs a generative classifier (Naive Bayes) and a discriminative classifier (Support Vector Machine) as base classifiers, to take advantage of both methods. Furthermore, we introduce a pair of weight parameters to balance the impact of labeled and pseudo-labeled data, and define a hybrid objective function to tune their values during co-training. The final prediction is given by the combination of base classifiers, and we define a pseudo-validation set to regulate their weight. Additionally, we present a strategy of pseudo-labeled data selecting to deal with the class imbalance problem. Experimental results on six datasets show that our method performs much better in practice, especially when the amount of labeled data is small.
hybrid generative/discriminative methods classification co-training naive bayes support vector machine class imbalance