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FSDM 2010 : International Workshop on Feature Selection in Data Mining


When Jun 21, 2010 - Jun 21, 2010
Where Hyderabad, India
Submission Deadline Mar 19, 2010
Categories    data mining

Call For Papers

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


The workshop will feature a full day program at the PAKDD conference. A keynote lecture will be given by a renowned speaker, and contributions from accepted papers will be invited for presentation.

Papers must be in English and must be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Author’s instructions and style files can be downloaded at We recommend a maximum length of *10 pages* in this format, including figures, title pages, references, and appendices. We also welcome (shorter) papers presenting new ideas or thought provoking issues. In addition to being published in the workshop proceedings, revised versions of accepted papers will be most likely published as a special issue in an international book series. Papers should be submitted as PDF files using the submission site

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