Preface
This volume contains research papers accepted for presentation at the 1st International
Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled,
Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009 .
MLD’09 is devoted to multi-label learning, which is an emerging and promising
research topic of machine learning. In multi-label learning, each example is associated
with multiple labels simultaneously, which therefore encompasses traditional super-
vised learning (single-label) as its special case. Multi-label learning is related to various
machine learning paradigms, such as classification, ranking, semi-supervised learning,
active learning, multi-instance learning, dimensionality reduction, etc.
Initial attempts on multi-label learning date back to 1999 with works on multi-label
text categorization. In recent years, the task of learning from multi-label data has been
addressed by a number of methods adapted from various popular learning techniques,
such as neural networks, decision trees, k-nearest neighbors, kernel methods, ensemble
methods, etc. More impressively, multi-label learning has manifested its effectiveness in
a diversity of real-world applications, such as image/video annotation, bioinformatics,
web search and mining, music categorization, collaborative tagging, directed marketing,
etc.
The goal of MLD’09 is to provide an interactive forum for researchers and practi-
tioners interested in multi-label learning to share their minds. A total of 16 submissions
were received and each paper was rigorously reviewed by two PC members. Based
on the returned reviews, 12 papers were accepted for presentation. The papers discuss
state-of-the-art as well as new research directions of multi-label learning.
We greatly appreciate the many people who contributed to the successful organiza-
tion of MLD’09. First of all, we wish to sincerely thank all the authors who submitted
their work for consideration. We would also like to thank the Program Committee mem-
bers as well as external reviewers for their excellent efforts in keeping the review pro-
cess at high quality. Last, but not least, we would like to thank everybody who helped the
organization of MLD’09 and the production of this volume, especially ECML/PKDD
2009 General Chair, Dunja Mladenic, and Workshop Chair, Rayid Ghani.
August 2009 Grigorios Tsoumakas
Min-Ling Zhang
Zhi-Hua Zhou
