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A hybrid generative/discriminative method for semi-supervised classification
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DOI：10.1016/j.knosys.2012.07.020.png)
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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 pseudolabeled 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. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Co-training
Hybrid generative/discriminative methods
Naive Bayes
Support vector machine
Classification
Class imbalance
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