Shahrood University of Technology Journal of AI and Data Mining 2322-5211 7 1 2019 01 01 Exploiting Associations between Class Labels in Multi-label Classification 35 45 1125 10.22044/jadm.2018.5306.1652 EN Z. Mirzamomen Computer Engineering Department, Shahid Rajaee Teacher Training University, Tehran, Iran. Kh. Ghafooripour Computer Engineering Department, Shahid Rajaee Teacher Training University, Tehran, Iran. Journal Article 2017 03 08 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 phases can bring about significant improvements. In this paper, we have introduced positive, negative and hybrid relationships between the class labels for the first time and we have proposed a method to extract these relations for a multi-label classification task and consequently, to use them in order to improve the predictions made by a multi-label classifier. We have conducted extensive experiments to assess the effectiveness of the proposed method. The obtained results advocate the merits of the proposed method in improving the multi-label classification results. https://jad.shahroodut.ac.ir/article_1125_72a8a3107e57a363501493b37afca38f.pdf