exploiting associations between class labels in multi-label classification

Multi-label classification has many applications in the text categorization, biology, and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. as it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phase can bring about significant improvements. in this paper, we introduce positive, negative, and hybrid relationships between the class labels for the first time, and propose a method to extract these relations for a multi-label classification task, and to use them consequently in order to improve the predictions made by a multi-label classifier. we conduct extensive experiments to assess the effectiveness of the proposed method. the results obtained advocate the merits of the proposed method in improving the multi-label classification results.