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***WE APOLOGIZE IF YOU RECEIVED MULTIPLE COPIES OF THIS MESSAGE.*** International Workshop on Feature Selection for Data Mining - Interfacing Machine Learning and Statistics in conjunction with 2005 SIAM International Conference on Data Mining, April 23, 2005, Newport Beach, California 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 Arizona State University Tempe, AZ 85287-8809 Tel: 480-727-7349 Fax: 480-965-2751 Email: [log in to unmask] Robert Stine Statistic Department The Wharton School University of Pennsylvania Philadelphia, PA 19104-6340 Tel: 215-898-3114 Fax: 215-898-1280 Email: [log in to unmask] Leonardo Auslender SAS Institute 1430 Rt. 206 N Bedminster, NJ 07921 Tel: 908-470-0080 x 8217 Email: [log in to unmask] Proceedings Chair: Lei Yu Computer Science & Engineering Arizona State University Tempe, AZ 85287-8809 Tel: 480-727-7808 Fax: 480-965-2751 Email: [log in to unmask] Program Committee Please refer to the workshop website. Paper Format, Important Dates, and Submission A paper (maximum 10 pages in single column, no smaller than 11 pt) should be submitted in PDF or WORD format Submissions should be emailed to [log in to unmask] Quality short papers are also welcome. The deadline for submission: January 7, Friday 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 .