> ***WE APOLOGIZE IF YOU RECEIVED MULTIPLE COPIES OF THIS MESSAGE.***
> CALL FOR PAPERS
>
> International Workshop on Feature Selection for Data Mining
> - Interfacing Machine Learning and Statistics
>
> Tempe, Arizona (the Grand Canyon State) on December 10, 2004
>
> The workshop website: http:enpub.eas.asu.edu/workshop04
>
> Knowledge discovery and data mining (KDD) is a multidisciplinary effort to
> mine nuggets of knowledge from data. The increasingly large data sets from
> many application domains have posed unprecedented challenges to KDD; in the
> meantime, new types of data are evolving such as Web, text, and microarray
> data. Research in computer science, engineering, and statistics confront
> similar issues in feature selection, and we see a pressing need for the
> interdisciplinary exchange and discussion of ideas. We anticipate that the
> resulting collaboration will lead in ultimately new directions and generate
> breakthroughs. While some progress has been made in this direction, a chasm
> divides these disciplines, each of them content with their limited views and
> slow to recognize accomplishments of those "across the street".
>
> This workshop aims to bring together researchers from different disciplines
> and further the collaborative research in feature selection. Feature
> selection is an essential step in successful data mining applications.
> Feature selection has practical significance in many areas such as
> statistics, pattern recognition, machine learning, and data mining (including
> Web, text, image, and microarrays). The objectives of feature selection
> include: building simpler and more comprehensible models, improving data
> mining performance, and helping to prepare, clean, and understand data. Some
> representative workshop topics and associated research issues are, but not
> limited to, the following.
>
> Feature ranking
> Subset selection
> Dimensionality reduction
> Feature construction
> Improving data mining performance
> Issues with data types and sizes
> Selection for labeled and unlabeled data
> Modeling variable and feature selection
> Evaluation measures Search methods
> Selection bias
> Sampling methods
> Model selection
> Case studies and applications
> Streaming data reduction
> Comparative studies
> Integration with data mining algorithms
> Emerging challenges
>
> Workshop Chairs
>
> Huan Liu
> Computer Science & Engineering
> Arizona State University
> Tempe, AZ 85287-8809
> Tel: 480-727-7349
> Fax: 480-965-2751
> Email: hliu@asu.edu Robert Stine
> Statistic Department
> The Wharton School
> University of Pennsylvania
> Philadelphia, PA 19104-6340
> Tel: 215.898.3114
> Fax: 215.898.1280
> Email:stine@wharton.upenn.edu Leonardo Auslender
> SAS Institute
> 1430 Rt. 206 N
> Bedminster, NJ 07921
> Tel: 908 470 0080 x 8217
> Email: leonardo.auslender at sas.com
>
>
> Local Arrangement Chair:
> Lei Yu (leiyu@asu.edu)
>
> Program Committee
> To be formed.
>
> Extended Abstract Format, Important Dates, and Submission
> * An abstract (maximum 2 pages) should be submitted in PDF or WORD format
> (submission details available on the workshop website)
> * The font should be no smaller than 11pt.
> * The deadline for submission is October 11, Monday.
> * The accepted abstracts will be published on CDs as well as on the
> workshop website.
> * Presentations will be considered for a special issue in a prestigious
> journal.
>
> More information can be found at the workshop website
> http://enpub.eas.asu.edu/workshop04.
>
>
>
> Leonardo Auslender, Member Appl. Staff
> SAS Institute. 908 470 0080 x 8217
>
> Omne tulit punctum/qui miscuit utile dulci/
> lectorem delectando/pariterque monendo.
>
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