Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning

The MIMLSVM algorithm is to transform the MIML learning problem into a single-instance multi-label learning problem, which is used as a bridge to degenerate into a single-instance single-label learning. However, this degradation algorithm is relatively easy to understand, but in the degradation process will lose some information, affecting the classification effect. By using multi-tasking learning, E-MIMLSVM+ is used to combine tag relevance to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, the paper improves MIMLSVM algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.