-------- Original-Nachricht -------- Betreff: CFP - FSDM 2006 International Workshop on Feature Selection for Data Mining Datum: Wed, 23 Nov 2005 10:10:58 -0700 Von: Huan Liu hliu@asu.edu Firma: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com

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

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@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@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@cs.binghamton.edu)

Program Committee To be available 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@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.

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