Affiliations: [a] College of Computer Science and Technology, Henan Polytechnic University, Henan, China | [b] CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal | [c] Safety and Emergency Management Research Center, Henan Polytechnic University, Henan, China | [d] Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
Correspondence:
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Corresponding author: Ning Chen, College of Computer Science and Technology, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, Henan 454003, China. E-mail: [email protected].
Abstract: In the context of expensive and time-consuming acquisition of reliably labeled data, how to utilize the unlabeled instances that can potentially improve the classification accuracy becomes an attractive problem with significant importance in practice. Semi-supervised classification that fills the gap between supervised learning and unsupervised learning is designed to take advantage of the unlabeled data in regular supervised learning procedure for classification tasks. In this paper we proposed a self-learning framework, that firstly pre-learns a classification model using the labeled data, then makes the prediction of unlabeled instances in the form of soft class labels, and re-learned a model based on the enlarged training data. Two multi-label Learning Vector Quantization Neural Networks (LVQ-NNs) are proposed, namely multi-label online LVQ-NN (mLVQo) and multi-label batch LVQ-NN (mLVQb), to work with the soft labels of training instances. The experiments demonstrate that the semi-supervised models using multi-label LVQ-NN as the base classifier can produce better generalization accuracy than the supervised counterpart.
