Semi-supervised Text Classification Using RBF Networks

Semi-supervised text classification has numerous applications and is particularly applicable to the problems where large quantities of unlabeled data are readily available while only a small number of labeled training samples are accessible. The paper proposes a semi-supervised classifier that integrates a clustering based Expectation Maximization (EM) algorithm into radial basis function (RBF) neural networks and can learn for classification from a very small number of labeled training samples and a large pool of unlabeled data effectively. A generalized centroid clustering algorithm is also investigated in this work to balance predictive values between labeled and unlabeled training data and to improve classification accuracy. Experimental results with three popular text classification corpora show that the proper use of additional unlabeled data in this semi-supervised approach can reduce classification errors by up to 26%.