CALL FOR PAPERS
International Workshop on
Feature Selection for Data Mining
- Interfacing
Machine Learning and Statistics
in conjunction with 2005
NOTE: The workshop email account fsdm@shanghai.eas.asu.edu
experienced some difficulty to receive email submission. Our sincere apologies
for the inconvenience caused to the authors. If you submitted your work and
have not received any acknowledgment from us, please kindly resubmit your work
to huan.liu@asu.edu
The submission due date is extended to January
21, 2005.
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 Tel: 480-727-7349 Fax: 480-965-2751 Email: hliu@asu.edu |
Robert Stine Statistic Department The Tel: 215.898.3114 Fax: 215.898.1280 Email:stine@wharton.upenn.edu |
Leonardo Auslender SAS Institute 1430 Rt. 206 N Tel: 908 470 0080 x 8217 Email:leonardo.auslender@sas.com |
Proceedings Chair and
Web Master: Lei Yu
(leiyu@asu.edu)
Program Committee
Please refer to the workshop website.
Paper Format,
Important Dates, and Submission
More
information can be found at the workshop website http://enpub.eas.asu.edu/workshop.