FSDM 2008

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FSDM 2008
Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery
Dates Sep 15, 2008 (iCal) - Sep 15, 2008
Homepage: bioinformatics.psb.ugent.be/fsdm2008
Location: Antwerp, Belgium
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Important dates
Submissions: Jun 16, 2008
Notification: Jul 16, 2008
Table of Contents

    Workshop on New Challenges for Feature Selection in
               Data Mining and Knowledge Discovery

                       FSDM 08

          Antwerp (Belgium) September 15, 2008


 The Workshop on new challenges for feature selection in data
 mining and knowledge discovery (FSDM2008) serves as a forum
 for researchers in the fields of statistics, pattern
 recognition, machine learning, and data mining to exchange
 ideas and present recent work. FSDM 2008 will be organised
 jointly by Ghent University, The University of the Basque
 Country, the University of Li?ge and Arizona State University,
 in collaboration with the European Conference on Machine
 Learning and Principles and Practice of Knowledge Discovery
 in Databases (ECML-PKDD 2008).

                     Workshop Scope

 The workshop invites papers relevant to research in feature
 selection in the broad sense, and especially welcomes
 contributions that highlight emerging feature selection
 challenges associated with new mining tasks.

 Possible paper topics include, but are not limited to:

 Dimensionality reduction                   Feature weighting
 Feature ranking                            Subset selection
 Feature extraction/construction            Feature selection methodology
 Integration with data mining algorithms    Ensemble methods
 Novel data structures                      Selection in small sample domains
 Data streams and time series               Feature selection bias and variance

 Feature selection for labeled and unlabeled data
 Modeling variable and feature selection
 Selection in extremely high-dimensional domains
 Novel univariate and multivariate metrics for feature selection
 Pitfalls and learned lessons in feature selection studies
 Real-world case studies and applications that highlight the role of
   feature selection
 Cross-discipline comparative studies (different types of bio-data, text,
   Web, ...)

                      Key Dates

     Paper Submission deadline: June 16th
     Author Notification: July 16th
     Final version of papers: July 31st
     Workshop: September 15th

                   Workshop Format

 The workshop will feature a full day program at the ECML-PKDD
 conference. A keynote lecture will be given by a renowned speaker,
 and contributions from accepted papers will be invited for

                   Paper submission

 Papers must be in English and must be formatted according to
 the Springer-Verlag Lecture Notes in Artificial Intelligence
 guidelines. Authors instructions and style files can be
 downloaded at http://www.springer.de/comp/lncs/authors.html.
 We recommend a maximum length of * 12 pages* in this
 format, including figures, title pages, references, and

 We also welcome (shorter) papers presenting new ideas or
 thought-provoking issues.

 In addition to being published in the workshop proceedings,
 revised versions of accepted papers will be most likely
 published as a special issue in an international book series.

 Papers should be submitted as PDF files using the submission site



  Yvan Saeys (Ghent University)
  Huan Liu (Arizona State University)
  I?aki Inza (University of the Basque Country)
  Louis Wehenkel (University of Li?ge)
  Yves Van de Peer (Ghent University)

This CfP was obtained from WikiCFP

Facts about "FSDM 2008"
AcronymFSDM 2008 +
End dateSeptember 15, 2008 +
Event typeWorkshop +
Has coordinates51° 4' 1", 4° 18' 35"Latitude: 51.066980555556
Longitude: 4.3097666666667
Has location cityAntwerp +
Has location countryCategory:Belgium +
Homepagehttp://bioinformatics.psb.ugent.be/fsdm2008 +
IsAEvent +
NotificationJuly 16, 2008 +
Start dateSeptember 15, 2008 +
Submission deadlineJune 16, 2008 +
TitleWorkshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery +