ECML PKDD 2009
ECML/PKDD 2009 Workshop on
 
Learning from Multi-Label Data
 
September 7, 2009 - Bled, Slovenia
 
Home - Organization - Submission - Important Dates - Programme

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.

Monday 7 September 2009
11:45 - 11:50
Workshop opening
11:50 - 13:05
Session 1
13:05 - 14:05
Lunch break
14:05 - 15:20
Session 2
15:20 - 15:40
Coffee break
15:40 - 16:25
Poster session
16:25 - 17:00
Discussion & closing

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