Accepted Papers

• Evaluation of Distance Measures for Hierarchical Multi­Label Classification in Functional Genomics
      Darko Aleksovski, Dragi Kocev and Sašo Džeroski
• Combining Classifiers for Improved Multilabel Image Classification
      Martin Antenreiter, Ronald Ortner and Peter Auer
  
• A Simple Instance-Based Approach to Multilabel Classification Using the Mallows Model
      Weiwei Cheng and Eyke Hüllermeier
• Sequence Learning from Data with Multiple Labels
      Mark Dredze, Partha P. Talukdar and Koby Crammer
  
• An Ensemble Method for Multilabel Classification using Transportation model
      Ehud Itach, Lena Tenenboim and Lior Rokach
  
• Curve prediction with Kernel Regression
      Damjan Kužnar, Martin Možina, and Ivan Bratko
  
• Generating Synthetic Multi-label Data Streams
      Jesse Read, Bernhard Pfahringer, and Geoff Holmes
  
• Ignoring Co-Occurring Sources in Learning from Multi-Labeled Data Leads to Model Mismatch
      Andreas P. Streich and Joachim M. Buhmann
  
• Multi-label Classification by Analyzing Labels Dependencies
      Lena Tenenboim, Lior Rokach, and Bracha Shapira
  
• Correlation-Based Pruning of Stacked Binary Relevance Models for
  Multi-Label Learning
      Grigorios Tsoumakas, Anastasios Dimou, Eleftherios Spyromitros, Vasileios Mezaris,     Ioannis Kompatsiaris, and Ioannis Vlahavas
  
• Weighted True Path Rule: a multilabel hierarchical algorithm for gene
  function prediction
      Giorgio Valentini and Matteo Re
  
• Using Unsupervised Classifiers for Multilabel Classification in Open-Class-
  Set Scenarios
      Santiago D. Villalba and Pádraig Cunningham

Proceedings

Here is the complete volume of the proceedings.

Schedule

The workshop will take place in the afternoon of September 7, 2009.

A tentative schedule follows.

Session 1: Algorithms

• A Simple Instance-Based Approach to Multilabel Classification Using the Mallows Model
      Weiwei Cheng and Eyke Hüllermeier
• Ignoring Co-Occurring Sources in Learning from Multi-Labeled Data Leads to Model Mismatch
      Andreas P. Streich and Joachim M. Buhmann
• Curve prediction with Kernel Regression
      Damjan Kužnar, Martin Možina, and Ivan Bratko

Session 2: Applications and Extensions

• Sequence Learning from Data with Multiple Labels
      Mark Dredze, Partha P. Talukdar and Koby Crammer
• Weighted True Path Rule: a multilabel hierarchical algorithm for gene
  function prediction
      Giorgio Valentini and Matteo Re
• Using Unsupervised Classifiers for Multilabel Classification in Open-Class-
  Set Scenarios
      Santiago D. Villalba and Pádraig Cunningham

Poster Session

• Evaluation of Distance Measures for Hierarchical Multi­Label Classification in Functional Genomics
      Darko Aleksovski, Dragi Kocev and Sašo Džeroski
• Combining Classifiers for Improved Multilabel Image Classification
      Martin Antenreiter, Ronald Ortner and Peter Auer
• An Ensemble Method for Multilabel Classification using Transportation model
      Ehud Itach, Lena Tenenboim and Lior Rokach
• Generating Synthetic Multi-label Data Streams
      Jesse Read, Bernhard Pfahringer, and Geoff Holmes
• Multi-label Classification by Analyzing Labels Dependencies
      Lena Tenenboim, Lior Rokach, and Bracha Shapira
• Correlation-Based Pruning of Stacked Binary Relevance Models for
  Multi-Label Learning
      Grigorios Tsoumakas, Anastasios Dimou, Eleftherios Spyromitros, Vasileios Mezaris,     Ioannis Kompatsiaris, and Ioannis Vlahavas