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
The workshop website: http://enpub.eas.asu.edu/workshop/2006
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
To be available on the workshop website.
Paper Format, Important Dates, and Submission
• A paper (maximum 8 pages in single column, no smaller than 11 pt) should
be submitted in PDF or WORD format
• Submissions should be emailed to featureselection at gmail.com
• Quality short papers, position papers are also welcome
• The deadline for submission: January 9, Monday.
• Acceptance notification: February 1, Wednesday
• Camera ready due: February, 14, Tuesday
• 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/2006.
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
www.cs.binghamton.edu/~lyu
