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    International Workshop on Feature Selection in Data Mining (FSDM10)

 

                  21st of June 2010, Hyderabad, India

                   (In conjunction with PAKDD 2010)

 

                 http://featureselection.asu.edu/fsdm10

 

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Knowledge discovery and data mining (KDD) is a multidisciplinary effort to
mine gold 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 social media, text, and
microarray data, to name a few. Researchers and practitioners in multiple
disciplines and various IT sectors confront similar issues in feature
selection, and there is a pressing need for continued exchange and
discussion of challenges and ideas, exploring new methodologies and
innovative approaches to generate breakthroughs.

 

Feature selection is effective in data preprocessing and reduction that is
an essential step in successful data mining applications. Feature selection
has been a research topic with 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 prepare, clean, and
understand data. Workshop on Feature Selection in Data Mining (FSDM2010)
aims to further the cross-discipline, collaborative effort in variable and
feature selection research. FSDM2010 will be held at the 14th Pacific-Asia
Conference on Knowledge Discovery and Data Mining (PAKDD 2010)

 

The workshop invites all papers related to feature selection, and especially
welcomes contributions that highlight emerging feature selection challenges
in data mining. Possible paper topics include, but are not limited to:

 

- Dimensionality reduction

- Feature weighting

- Feature ranking

- Subset selection

- Feature extraction/construction

- Feature selection methodology

- Integration with data mining algorithms

- Pitfalls and learned lessons in feature selection studies

- Novel data structures

- Selection in small sample domains

- Data streams and time series

- Feature selection bias and variance

- Selection in extremely high-dimensional domains

- Real-world case studies and applications that highlight the role of
feature selection

- Emerging challenges

 

Accepted papers will be published in JMLR: Workshop and Conference
Proceedings.

 

--- KEY DATES ---

Paper Submission deadline: March 19th, 2010

Author Notification: April 16th, 2010

Camera-ready: April 30th, 2010

Workshop: June 21th, 2010

 

--- ORGANIZATION ---

Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao