Multi-label learning vector quantization for semi-supervised classification
Ning Chen, Bernardete Ribeiro, Chaosheng Tang, An Chen · Intelligent Data Analysis · 2019
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 g ap 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.