Semi-supervised multi-label image classification based on nearest neighbor editing

Semi-supervised multi-label classification has been applied to many real-world applications such as image classification, document classification and so on. In semi-supervised learning, unlabeled samples are added to the training set for enhancing the classification performance, however, noises are introduced simultaneously. In order to reduce this negative effect, the nearest neighbor data editing technique is introduced to semi-supervised multi-label classification, and thus an algorithm named Multi-Label Self-Training with Editing (MLSTE) is proposed in this work. The proposed algorithm is able to solve the uncertainty problem in semi-supervised multi-label classification to some extent, by improving the performance of determining the label number and selecting confident samples during the course of semi-supervised learning. Extensive experimental results on several benchmark datasets have been carried out to verify the effectiveness of the proposed MLSTE algorithm. & 2013 Elsevier B.V. All rights reserved.