MLD 2009 : 1st International Workshop on learning from Multi-Label Data
Call For Papers
1st International Workshop on learning from Multi-Label Data
September 7, 2009 - Bled, Slovenia
Held in conjunction with ECML/PKDD 2009:
European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases
Multi-label learning deals with the problem where each example is associated with multiple labels and thus encompasses traditional supervised learning (single-label) as its special case.
Though methods for learning from multi-label textual data have been proposed since 1999, the recent years have witnessed an increasing number and diversity of applications, such as image/video annotation, bioinformatics, web search and mining, music categorization, collaborative tagging and directed marketing.
Learning from multi-label data stretches across several aspects of supervised learning tasks, including classification, ranking, semi-supervised learning, active learning and dimensionality reduction, and across several learning paradigms, such as decision trees, nearest neighbor classifiers, neural networks, ensemble methods, support vector machines, kernel methods, genetic algorithms, etc.
It poses several old and new research challenges, such as exploiting label correlation to improve predictive performance, exploiting structure and semantic relationships among the labels to improve predictive performance and computational efficiency, and scaling learning methods to very large number of labels and examples. In addition, multi-label learning is closely related to other learning frameworks, such as the newly proposed multi-instance multi-label learning (MIML).
AIMS & SCOPE
The goal of this workshop is to bring researchers and practicioners that work on various aspects of multi-label learning into a fruitful dicussion about the state-of-the-art and the remaining open problems, and to offer them an opportunity to identify new promising research directions. To achieve this goal we are soliciting two types of contributions: a) mature research results, and b) interesting preliminary results or stimulating position statements. In addition, the workshop will feature at least one discussion session to allow for a more interactive and engaging experience.
MAIN TOPICS OF INTEREST
* Classification of multi-label data
* Ranking of multi-label data
* Statistical characterizations of multi-label data sets
* Evaluation metrics for multi-label learning methods
* Exploiting label structure and relationships (trees, ontologies, etc)
* Learning label structure and relationships
* Learning from multiple continuous target variables
* Online learning from multi-label data
* Hierarchical multi-label classification and ranking
* Dimensionality reduction of multi-label data
* Clustering multi-label data
* Semi-supervised learning from multi-label data
* Learning association rules from multi-label data
* Scalable methods for learning with very large number of labels
* Multi-instance multi-label learning
* Active learning from multi-label data
* Applications of multi-label learning in bioinformatics
* Semantics annotation of images and video
* Multi-label learning from music
* Automated tag recommendation in collaborative tagging systems
* Submission : June 10, 2009
* Notification : June 30, 2009
* Camera ready : August 15, 2009
* Workshop day : September 7, 2009
The papers must be in English and must be formatted according to the Springer-Verlag LNCS/LNAI guidelines (available at http://www.springer.de/comp/lncs/authors.html). The maximum length of papers is at most 16 pages in this format. At the time of submission, the papers must not be under review or be accepted for publication elsewhere. Each submitted paper will be rigorously reviewed by at least two reviewers. The submission site for MLD'09 is managed by EasyChair (https://www.easychair.org/login.cgi?conf=mld09).
* Hendrik Blockeel, Katholieke Universiteit Leuven
* Johannes Furnkranz, TU Darmstadt
* Shantanu Godbole, IBM Research
* Jose M. Pena, Universidad Politecnica de Madrid
* Xian-Sheng Hua, Microsoft Research Asia
* Eyke Hullermeier, Philipps-Universitat Marburg
* Ioannis Katakis, Aristotle University of Thessaloniki
* Dragi Kocev, Jozef Stefan Institute
* Bernhard Pfahringer, University of Waikato
* Fadi Thabtah, University of Huddersfield
* Jieping Ye, Arizona State University
* Kai Yu, NEC Laboratories America, Inc.
* Shipeng Yu, Siemens Medical Solutions USA, Inc.
Department of Informatics,
Aristotle University of Thessaloniki, Greece
College of Computer and Information Engineering,
Hohai University, China
National Key Laboratory for Novel Software Technology,
Nanjing University, China