MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS

Probability Estimation for Multi-class Classification Based on Label Ranking

We consider the problem of probability estimation in the setting of multi-class classification. While this problem has already been addressed in the literature, we tackle it from a novel perspective. Exploiting the close connection between probability estimation and ranking, our idea is to solve the former on the basis of the latter, taking advantage of recently developed methods for label rank...

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 phases ...

Multi-label classification by exploiting label correlations

Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e Sch...

Case-Based Label Ranking

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propose a sophisticated k-NN framework as an alternative to previous binary decomposition techniques. It exhibits the appealing property of transparency and is based on an aggregation model which allows to incorporate a br...

A new parametric method for ranking fuzzy numbers based on positive and negative ideal solutions