Due on Jan. 21,2005: SIAM (SDM-05) Workshop on Feature Selection for Data Mining

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NOTE: If you submitted your work and have not received any acknowledgment from us, please check the workshop website for latest updates.

 

*****SIAM DM 2005 Workshop on Feature Selection*****

 

International Workshop on Feature Selection for Data Mining - Interfacing Machine Learning and Statistics

 

in conjunction with 2005 SIAM International Conference on Data Mining, April 23, 2005, Newport Beach, California

 

Paper Format, Important Dates, and Submission

 

A paper (maximum 10 pages in single column, no smaller than 11 pt) should be submitted in PDF or WORD format.
Quality short papers (4 pages) or extended abstracts (2 pages) are also welcome.
Submissions should be emailed to huan.liu@asu.edu
The deadline for submission: January 21, 2005, Friday.
The accepted papers will be published in the workshop proceedings.
Accepted papers 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/workshop .

 

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 our collaborations will shed new lights on research directions and approaches, and lead to breakthroughs.

 

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@sas.com

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