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International
Workshop on Feature Selection for Data Mining:
Interfacing Machine Learning and Statistics
April 22, 2006, Bethesda, Maryland
in conjunction with
2006 SIAM Conference on
Data Mining (SDM)
Workshop
Proceedings available
online.
Knowledge discovery and data mining (KDD) is a
multidisciplinary effort to extract nuggets of information fromdata.
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 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.
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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
False discovery rates
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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
Survival analysis
Connecting selection and causality
Knowledge in feature selection
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There is no separate workshop
registration. Please visit SIAM DM 2006 website for
registration.
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@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.
This workshop follows the previous highly
successful workshop: FSDM 2005, held in
Newport Beach, CA.
For more information about
FSDM 2006, please contact us.
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