Special Issue of Neurocomputing journal on Solving Complex Machine Learning Problems with Ensemble Methods (COPEM)

Impact Factor: 1.634
5-Year Impact Factor: 1.811

Aims and Scope
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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 special issue aims to gather research works in the area of ensemble methods to present the latest results obtained
and the efforts of the community to address difficult machine learning problems.

We cordially invite all potential authors to submit original and unpublished papers to this issue. The emphasis of the
papers published in this issue will be on novel and original research. All papers will be peer reviewed.

Note that a number of papers will be invited from the COPEM workshop co-located with ECML/PKDD 2013 (http://ama.imag.fr/COPEM/). However, this is an open call for all authors interested to publish their high quality work. All papers (including invited papers) will go through a peer-review process.

Topics of Interest
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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 Learning
* Multi-Label Classification
* Data-stream classification and Concept Drift adaptation
* Multi-Dimensional Classification
* Feature Selection
* Active Learning
* Mining social networks
* Applications of Ensemble Methods
* Clustering Ensembles

Important dates
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* Full paper submission: December 9, 2013
* First editorial notice: January 31, 2014
* Revised version submission: March 1, 2014
* Final decision: April 1, 2014

Instructions for Submissions
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Information on paper preparation can be found in http://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors

Authors must submit their papers electronically by using online manuscript submission system located at http://ees.elsevier.com/neucom/

Please choose the special issue COPEM for your submission.

To ensure that all manuscripts are correctly included into the special issue described, it is important that all
authors select this special issue when they reach the "Article Type" step in the submission process.

Guest Editors
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Dr Ioannis Katakis: (katak@di.uoa.gr),
Department of Informatics and Telecommunications,
National and Kapodistrian University of Athens,
Panepistimiopolis, Ilissia, Athens 15784, Greece.

Dr Daniel Hernández-Lobato: (daniel.hernandez@uam.es),
Computer Science Department,
Universidad Autónoma de Madrid,
Calle Francisco Tomás y Valiente 11, 28049, Madrid, Spain.

Professor Gonzalo Martínez-Muñoz: (gonzalo.martinez@uam.es),
Computer Science Department,
Universidad Autónoma de Madrid,
Calle Francisco Tomás y Valiente 11, 28049, Madrid, Spain.

Dr Ioannis Partalas: (ioannis.partalas@imag.fr),
University of Grenoble,
Centre Equation 4 - UFR IM2AG - LIG/AMA BP 53 - F-38041 Grenoble Cedex 9, France.