International Workshop on Feature
Selection for Data Mining:
in conjunction with
This workshop on Feature Selection is held with the SIAM International Conference on Data Mining (SDM05), April 21 - 23, 2005 at the Sutton Place Hotel, in Newport Beach, California.
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 collaboration will lead in ultimately new directions and generate breakthroughs.
This workshop aims to further the cross-discipline, collaborative effort in variable and feature selection research. Variable and feature selection is effective in data preprocessing and reduction that is an essential step in successful data mining applications. Variable and 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 variable and feature selection include: building simpler and more comprehensible models, improving data mining performance, and helping to prepare, clean, and understand data. See original Call For Papers for topics of interests.
For more information about FSDM 2005, please contact us.