Third Workshop on
ROC Analysis in ML
Pittsburgh, USA,  29 June, 2006

held within ICML’2006, the 23rd International Conference on Machine Learning

Brief description

Receiver Operating Characteristic Analysis (ROC Analysis) is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. Widely used in medicine for many decades, it has been introduced relatively recently in machine learning. In this context, ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. Furthermore, the Area Under the ROC Curve (AUC) has been shown to be a better evaluation measure than accuracy in contexts with variable misclassification costs and/or imbalanced datasets. AUC is also the standard measure when using classifiers to rank examples, and, hence, is used in applications where ranking is crucial, such as campaign design, model combination, collaboration strategies, and co-learning.

Nevertheless, there are many open questions and some limitations that hamper a broader use and applicability of ROC analysis. Its use in data mining and machine learning is still below its full potential. An important limitation of ROC analysis, despite some recent progress, is its possible but difficult extension for more than two classes.

This workshop follows up a first workshop (ROCAI'04) held within ECAI-2004 and a second workshop (ROCML'05) held within ICML-2005. This third workshop is intended to investigate on the hot topics identified during the two previous workshops (e.g. multiclass extension, statistical analysis, alternative approaches), on the one hand, and to encourage cross-fertilisation with ROC practitioners in medicine, on the other hand, thanks to an invited medical expert.

Dr. Darrin C. Edwards, Department of Radiology, University of Chicago will present the state of the art of ROC analysis in radiology. Moreover a three-class medical dataset is available to support exchanges between medical experts and participants. We strongly encourage submissions dealing with the provided medical dataset. The three-class medical dataset will be available on this webpage soon. Please send an email at lachiche@lsiit.u-strasbg.fr if you want to join the mailing list where updates will be sent.

Organisation

We encourage submissions on hot topics raised during previous editions, e.g. ROC analysis software repository, multiclass extension, statistical analysis. To promote a workshop atmosphere the program committee will select a few topics and relevant papers. The program and accepted papers will be published on the workshop website before the call for participation where we will encourage people to read them in order to focus on the discussion during the workshop. The authors of accepted papers will be asked to prepare the discussion rather than to detail their paper, by summarising their contributions, on the one hand, and by comparing them to the other points of view presented in their session, on the other hand.

Important Dates

• Deadline for the workshop papers:  5  May 2006
• Notification of acceptance:              24 May 2006
• Camera Ready copies:                      7  June 2006
• Workshop dates:                              29 June 2006
  

• List of Accepted Papers
• Workshop Proceedings
  

Schedule

• Schedule

Workshop Program Committee

• Stephan Dreiseitl FHS Hagenberg, Austria.
  
• Richard M. Everson, University of Exeter, UK.
• Cèsar Ferri, Technical University of Valencia, Spain.
• Jonathan E. Fieldsend, University of Exeter, UK.
• Peter Flach, University of Bristol, UK.
• José Hernández-Orallo, Technical University of Valencia, Spain.
• Rob Holte, University of Alberta, Canada.
• Nicolas Lachiche, University of Strasbourg, France.
• Michele Sebag,  LRI, CNRS-Université de Paris Sud , France.
  
• Sofus A. Macskassy,  Fetch Technologies, Inc., USA.
• Alain Rakotomamonjy, Insa de Rouen, France.
• Francesco Tortorella, University of Cassino, Italy.

Workshop Organizing Committee

• Cèsar Ferri, Technical University of Valencia, Spain.
• Nicolas Lachiche, University of Strasbourg, France (contact person).
• Sofus A. Macskassy,  Fetch Technologies, Inc., USA.
  

Submission Guidelines

Potential participants are invited to submit papers according to one of the following formats:

• Position papers :  EITHER short paper (2-4 pages) specifically written for the purposes of this workshop that highlight open problems and/or new applications of ROC analysis, OR, in order to get a view as wide and representative as possible of current works, full length research papers published or submitted to recent major conferences are acceptable as position papers if they are clearly submitted as position papers stating where they were submitted/published.
   
• Research papers : these papers (up to 8 pages) present technical contributions on ROC analysis in Machine Learning and should follow ICML main conference standard.

Authors should submit their papers electronically (PDF or PS format) to the contact person (lachiche@lsiit.u-strasbg.fr) It is recommended to submit papers using the final camera-ready ICML 2006 conference paper style, including author names.

Some relevants links (introduction to ROC analysis, software, etc.)

• The ROC Convex Hull: software and papers, mantained by Tom Fawcett.
• Receiver Operating Characteristic (ROC) Literature Research: ROC papers and articles, mantained by Kelly Zou.
  
• The Magnificent ROC: A nice introduction to ROC Analysis.
• ROC Analysis Software.
• JROCFITi: A ROC Analysis Applet.
• ROCOn: A tool for visualising ROC graphs.
• Very introductory slides on ROC Analysis.
• Tutorial on "The Many Faces of ROC Analysis in Machine Learning" by Peter Flach
• ROCR: An R package on ROC Analysis
• ROCTools: a comprehensive software to help researchers and scientists in their ROC analysis.