The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 514).
The Concept
Multi-label data consist of instances that are associated with a vector of binary target variables. The last 10 years, the topic of learning from multi-label data has witnessed enormous progress, evident by the increasing number of papers dealing with this topic, as well as by the fact that it has recently started to appear as a distinct topic in top conferences like KDD, AAAI and ICML. Despite all this amount of work, several challenges still arise when applying multi-label learning in real-world applications and industrial settings. The main goal of AMULET is to develop novel multi-label learning techniques to deal with two such key under-addressed challenges, paving the way for a wider adoption of multi-target prediction in complex real-world tasks: i) concept evolution, ii) interpretability.