MLJ Special Issue on Learning from Multi-Label Data

[Grigorios Tsoumakas]

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Learning from Multi-Label Data

Special Issue of the Machine Learning Journal

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Traditional supervised classification works under a single-target
scenario, i.e. each example (data object) is associated with one
single nominal target variable characterizing its property. However,
an increasing number of practical applications, such as the
(semi)automated annotation of large collections of image/video, music,
text, web and biology objects, drug discovery, query categorization,
medical diagnosis, tag recommendation and direct marketing, involve
data with multiple binary target variables, called multi-label data.

The ever-increasing interest on learning from multi-label data (i.e.
multi-label learning) is witnessed by the significant amount of work
in terms of learning theories, novel algorithms and new applications.
Despite this encouraging progress, there are still many open issues to
be addressed in this emerging learning scenario. The purpose of this
special issue is to solicit recent innovative and promising research
findings on multi-label learning. Topics of interest include, but are
not limited to:

* Theoretical analysis of multi-label learning
* Novel methodologies/algorithms to learn from multi-label data
* Scalable multi-label learning (presence of large number of labels)
* Learning and/or exploiting label structure (constraints,
hierarchies, ontologies)
* Learning from data with multiple non-binary target variables
(nominal, real-valued, mixed)
* Evaluation of multi-label learning
* New applications of multi-label learning
* Related learning tasks
o Feature selection from multi-label data
o Multi-instance multi-label learning (MIML)
o Active multi-label learning
o Semi-supervised multi-label learning


Tentative Schedule
* Submission Deadline: September 30, 2010
* Decisions Announced: February 15, 2011
* Camera-Ready Due: April 30, 2011
* Print Publication: to be announced


Guest Editors
* Grigorios Tsoumakas, Aristotle University of Thessaloniki
* Min-Ling Zhang, Hohai University
* Zhi-Hua Zhou, Nanjing University


More Information
http://mlkd.csd.auth.gr/events/ml2010si.html