Estimating latent relative labeling importances for multi-label learning

• Main Authors: He, Shuo, Feng, Lei, Li, Li
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Multi-label Learning; Relevant Labels
• Online Access: https://hdl.handle.net/10356/143866
• Institution: Nanyang Technological University

In multi-label learning, each instance is associated with multiple labels simultaneously. Most of the existing approaches directly treat each label in a crisp manner, i.e. one class label is either relevant or irrelevant to the instance. However, the latent relative importance of each relevant label...