ID  - loza10pt
T1  - Multilabel Classification in Parallel Tasks
A1  - Loza MencĂ­a, Eneldo
ED  - Zhang, Min-Ling
ED  - Tsoumakas, Grigorios
ED  - Zhou, Zhi-Hua
TI  - Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010
Y1  - 2010
SP  - 29
EP  - 36
CY  - Haifa, Israel
UR  - http://www.ke.tu-darmstadt.de/publications/papers/loza10mlpt.pdf
N2  - In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations between domains. Following the multi-task learning setting, in which multiple similar tasks are learned in parallel, we propose a global learning approach that jointly  considers all domains. It is empirically demonstrated in this work that this approach is effective despite its simplicity when using a multilabel learner that takes label correlations into account.
ER  -