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COPEM 2013 : ECML/PKDD 2013 Workshop - COPEM - Solving Complex Machine Learning Problems with Ensemble Methods


When Sep 24, 2013 - Sep 24, 2013
Where Prague
Submission Deadline Jul 4, 2013
Notification Due Jul 19, 2013
Final Version Due Aug 2, 2013
Categories    machine learning   data mining

Call For Papers

COPEM - ECML/PKDD 2013 Workshop - Submission deadline: 06/28/2013

*** Extended Deadline : July 4th ***

Call for Papers - COPEM - Solving Complex Machine Learning Problems
with Ensemble Methods


Workshop at ECML-PKDD 2013, September 27, Prague, Czech Republic

**** Extended versions of selected papers will be considered for a Special Issue in Neurocomputing journal (Elsevier, ***

Important Dates

*****Workshop paper submission deadline: June 28, 2013
*****Workshop paper acceptance notification: July 19, 2013
*****Workshop paper camera-ready deadline: Aug 2, 2013
*****Extended versions of selected papers for journal Special Issue: Nov 2, 2013


By combining the decisions of several different predictors, ensemble
methods provide appealing solutions to challenging problems in machine
learning. These include for example dealing with learning under non-standard
circumstances, i.e., when large volumes of data are available for induction,
or when a data stream has to be classified under the phenomenon of concept
drift. Similarly, ensemble methods can be used to tackle difficult problems
related to multi-label classification, feature selection, or active learning.
Although research in the field of ensemble learning has grown considerably in
the recent years, the specific application of ensemble methods to the problems
described is still in a very early stage. There are still many open issues
and there remain challenges which may require interdisciplinary approaches.
This workshop aims to gather together researchers in the area of ensemble
methods to present their latest work and their efforts to address difficult
machine learning problems, to discuss the challenges in the field and to
identify where to target our efforts as a research community. Additionally,
one of the goals of the workshop is to initiate collaborations between
experts in ensemble methods and non-experts. In order to achieve this
objective, the workshop includes a scientific networking component, where
challenging machine learning problems can be submitted for discussion.

Topics of Interest

Researchers are encouraged to submit papers focusing on how to use
ensemble methods to tackle difficult machine learning problems including,
but not restricted to the following topics:

* Large Scale Learning
* Multi-modal Classification
* Multi-Label Classification
* Data-stream classification and Concept Drift adaptation
* Multi-Dimensional Classification
* Feature Selection
* Active Learning
* Mining social networks
* Applications of Ensemble Methods

Submission Instructions

Two types of submissions are invited: paper submissions and problem

Paper submissions must be written in English and formatted according to
the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.
Authors instructions and style files can be downloaded at:

Two types of paper submissions are allowed: short papers and research
papers. The maximum length of short papers is 6 pages in the format described
before. The maximum length of research papers is 16 pages, although papers of
up to 12 pages are preferred. Submitted papers will be peer-reviewed by at
least three reviewers. Acceptance will be based on the basis of these reviews
and on relevance, technical soundness, originality, and clarity of presentation.
Accepted papers will be presented at the workshop either as a poster or via
oral presentation.

Submissions must be made through EasyChair system

**** Extended versions of selected papers will be considered for a Special Issue in Neurocomputing journal (Elsevier, ***

Problem submissions have the objective to start interactions in the machine
learning community. Researchers are invited to submit:

* A problem that they intend to conduct research on and they believe
ensemble methods can be a suitable approach but have no expertise in
the field.

* A problem that they have already investigated using a probably
simple ensemble strategy but they wish to improve it.

Problem submissions can be uploaded in the workshop web site as either:

* A document (extended abstract with the ability to add attachments).
* A video presentation.

via the following link:

A message board will be enabled under each problem to facilitate discussion
between the different members of the machine learning community.

Problem submission will be briefly presented in a Networking Session of the
workshop by the authors and/or the workshop chairs. The goal will be then to
obtain feedback from the community of experts in ensemble methods attending
the workshop.


* Ioannis Katakis: Department of Communication and Internet Studies, Cyprus University of Technology
* Daniel Hernández-Lobato: Computer Science Department, Universidad Autónoma de Madrid
* Gonzalo Martínez-Muñoz: Computer Science Department, Universidad Autónoma de Madrid
* Ioannis Partalas: Laboratoire d'Informatique de Grenoble, University of Grenoble

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