[Computational-biology] CFP - FSDM 2006: International Workshop on Feature Selection for Data Mining

Lei Yu lyu at cs.binghamton.edu
Wed Dec 21 13:51:21 EST 2005

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

in conjunction with 2006 SIAM Data Mining Conference, April 22, 2006
Bethesda, Maryland


Knowledge discovery and data mining (KDD) is a multidisciplinary effort
to extract nuggets of information from data. Massive data sets have
become common in many applications and pose novel challenges for KDD.
Along with changes in size, the context of these data runs from the
loose structure of text and images and to designs of microarray
experiments. Research in computer science, engineering, and statistics
confront similar issues in feature selection, and we see a pressing need
for and benefits in the interdisciplinary exchange and discussion of
ideas. We anticipate that our collaborations will shed light on research
directions and provide the stimulus for creative breakthroughs.

This workshop will bring together researchers from different disciplines
and encourage 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. The
objectives of feature selection include: building simpler and more
comprehensible models, improving data mining performance, and helping to
prepare, clean, and understand data.

Submissions that consider knowledge in feature selection will receive
special consideration. Knowledge here means some declarative knowledge
that can be explicitly expressed by a domain expert such as constraints.
One form of using knowledge is semi-supervised learning. The
semi-supervised situation remains prevalent, even in the presence of
massive data sets. The high expense of “marking documents” leads to
situations in which one has massive data describing the feature space,
but relatively little describing the relationship between features and
the response. We encourage presentations featuring both the theory
behind feature selection as well as novel applications to data.
Additional workshop topics include the following.

- Dimensionality reduction
	(feature ranking, subset selection, feature extraction)
- Feature construction
- Improving data mining performance
- Novel data structures
- Streaming data reduction and time series
- Selection for labeled and unlabeled data
- Modeling variable and feature selection
- Goodness measures and evaluation (e.g., false discovery rates)
- Ensemble methods
- Selection bias
- Sampling methods
- Selection with small samples
- Cross-discipline comparative studies (microarray, text, Web)
- Integration with data mining algorithms
- Real-world case studies and applications
- Emerging challenges
	(e.g., survival analysis, connecting selection and causality,
knowledge in feature selection)

Workshop Chairs

Huan Liu
Computer Science & Engineering
Arizona State University
Tempe, AZ 85287-8809
Tel: 480-727-7349
Fax: 480-965-2751
Email: hliu at 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 at 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

Proceedings and Publicity Chair: Lei Yu (lyu at cs.binghamton.edu)

Program Committee
	Available at the workshop website.

Paper Format, Important Dates, and Submission
 	o A paper (maximum 8 pages in single column, no smaller than 11 pt)
          should be submitted in PDF or WORD format
	o Submissions should be emailed to featureselection at gmail.com
	o Quality short papers, position papers are also welcome
	o The deadline for submission: January 9, Monday.
	o Acceptance notification: February 1, Wednesday
	o Camera ready due: February, 14, Tuesday
	o The accepted papers will be published in the workshop proceedings.
	o Accepted papers will be considered for a special issue in a
          prestigious journal.

More information can be found at the workshop website

Lei Yu
Assistant Professor
Department of Computer Science
Thomas J. Watson School of Engineering and Applied Science
State University of New York at Binghamton
P.O. Box 6000
Binghamton, NY 13902-6000

Voice: (607) 777-6250
Fax: (607) 777-4729

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