CFP

Call for Papers Machine Learning journal Special Issue on Data Mining Lessons Learned

Guest editors: Nada Lavrac, Hiroshi Motoda and Tom Fawcett

Many data mining techniques have emerged for analyzing and visualizing large volumes of data, and what we see in the technical literature are mostly success stories of these techniques. We rarely hear of steps leading to success, failed attempts, or critical representation choices made; and rarely do papers include expert evaluations of achieved results. Insightful analyses of successful and unsuccessful applications are crucial for increasing our understanding of machine learning techniques and their limitations.

Challenge problems (such as the KDD Cup, COIL and PTE challenges) have become popular in recent years and have attracted numerous participants. These challenge problems usually involve a single difficult problem domain, and participants are evaluated by how well their entries satisfy a domain expert. The results of such challenges can be a useful source of feedback to the research community.

At ICML-2002 a workshop on Data Mining Lessons Learned was held and was well attended. This special issue of the Machine Learning journal follows the main goals of that workshop. The aim of this special issue is to collect the experience gained from data mining applications and challenge competitions. We are interested in lessons learned both from successes and from failures. Authors are invited to report on experiences with challenge problems, experiences in engineering representations for practical problems, and in interacting with experts evaluating solutions. We are also interested in why some particular solutions -- despite good performance -- were not used in practice, or required additional treatment before they could be used.

An ideal contribution to this special issue would describe in sufficient detail one problem domain, either an application or a challenge problem. Alternatively, a submission may analyze methodological aspects from individual developments, or may analyze a subfield of machine learning or a set of data mining methods to uncover important and unknown properties of a class of methods or a field as a whole.

Articles to appear in this special issue must satisfy the high standards of the Machine Learning journal. Submissions will be evaluated on novelty, generality, significance, support and clarity. Further information on evaluation criteria and the submission process are available at:

http://www.hpl.hp.com/personal/Tom_Fawcett/DMLL-MLJ-CFP.html

-- Tom Fawcett HP Laboratories Palo Alto, CA USA