CFP: Workshop on Probabilistic Graphical Models for Classification (within ECML/PKDD'03)

We think that with this set of papers we can have a qualified Workshop, covering different and interesting areas of our research topic.

As we have 3 slots of one hour and a half in the Workshop day (September 23, Tuesday), we plan to present-discuss 3 contributions in each slot. Each work will have in the Workshop day 20 minutes for its oral presentation, followed by 10 minutes for discussion-questions. You can see in the following lines the final distribution of the oral presentations of the papers:

1) M.G. Madden: "The Performance of Bayesian Network Classifiers Constructed using Different Techniques". National University of Ireland (Galway, Ireland)

2) E. Gyftodimos, P.A. Flach: "Hierarchical Bayesian Networks: an Approach to Classification and Learning for Structured Data". University of Bristol (UK)

3) J. Bala, K.C. Chang, Y. Weng: "A Hybrid Bayesian Decision Tree for Classification". DATAMAT System Research (McLean, VA, USA) - George Mason
University (Fairfax, VA, USA)

4) N. Sondberg-Madsen, C. Thomsen, J.M. Peña: "Unsupervised Feature Subset Selection". Aalborg University (Denmark)

5) P. Leray, I. Zaarour, L. Heutte, B. El Eter, J. Labiche, D. Mellier: "A Bayesian Network Model for Discovering Handwriting Strategies of Primary School Children". Université et INSA Rouen (France) - Université Libanaise (Beyrouth, Liban)

6) H-C. Kim, Z. Ghahramani: "The EM-EP Algorithm for Gaussian Process Classification". Gatsby Computational Neuroscience Unit, UCL (London, UK)

7) N. Ben Amor, S. Benferhat, Z. Elouedi: "Naive Bayesian Networks in Intrusion Detection Systems". Institut Supérieur de Gestion Tunis (Tunisie) - Université d'Artois (Cedex, France)

8) W. van Olmen, B. Naudts: "Feature Selection for Future Classes". University of Antwerp (Belgium)

9) M. Wiering: "Hierarchical Mixtures of Naive Bayesian Classifiers". Utrecht University (The Netherlands)

--> Workshop description

Probabilistic graphical model paradigm has become a popular tool for encoding, representing and handling uncertain knowledge in expert systems over the last decade. Applications of this paradigm include wide areas of the reality (medicine, agriculture, economy, bioinformatics...). Currently, interest is emerging within probabilistic graphical models to use them as a tool to induce supervised-unsupervised classification models. Taking the well-known naive-Bayes classifier as a basic, extensions and improvements of this simple but effective algorithm are being proposed from the field of probabilistic graphical models in the last ten years. The works of prestigious authors of the area of machine learning, have enhanced the role of probabilistic graphical models to solve classification tasks. Apart from the desired high accuracy of the model, these approaches offer the opportunity to graphically show the probabilistic relationships between domain attributes. Among these relevant approaches the selective Bayesian classifier, the Autoclass procedure, the tree-augmented network, discriminative vs. generative classifiers or exact model averaging with naive Bayes can be cited. These works, coupled with the spectacular recent development of the Bayesian network paradigm, are opening a wide range of possibilities to adapt probabilistic graphical models to solve classification tasks.

Contributed works in this workshop should ideally be in the area of new algorithmic, theoretical and application approaches in the use of probabilistic graphical models to solve supervised and unsupervised classification tasks.

--> Topics of interest

A non-exhaustive list of the research topics of this Workshop is the following:

--> Submission guidelines and further publication

Submissions should be sent by June 13, 2003, in the electronic format as a PDF os PostScript file to Inaki Inza (inza@si.ehu.es) with the subject "PGMs for Classification".

The papers should be formatted as for the main ECML/PKDD-2003 conference submissions. This is according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines. Springer-Verlag authors instructions and style files can be downloaded here. The maximum length of papers is 12 pages.

Submitted papers will be reviewed by referees from the Program Committee. Accepted papers will be published in the working notes provided by ECML/PKDD-2003. The Organization Committee of this Workshop is maintaining contacts with a prestigious international journal to schedule an special issue with the topic of this Workshop. Extended versions of the best papers of the Workshop will be invited to participate in this special issue.

The authors will be notified about the acceptance or rejection of their papers by July 4, 2003. Camera-ready versions of the papers are due July 11, 2003.

--> Attendance

The workshop audience is intended for researchers in the area of machine learning and probabilistic graphical models, including practitioners of knowledge discovery in databases and statistical and computational learning theorists. Intelligent systems researchers with an interest in real-world applications of data-mining will also find this workshop of interest. Researchers in the Bayesian network paradigm would also benefit from this workshop, enriching their knowledge about the great potential of the Bayesian network paradigm in supervised and unsupervised classification tasks. The workshop aims to bring together researchers in these fields in order to assess the current state of the art, to identify obstacles for progress and to determine future research directions. The workshop's aim is also to promote research collaboration among different groups in these fields.
However, attendance is not limited to the paper authors, and we strongly encourage interested researchers from related areas to attend the workshop.

--> Key dates:

Submission Deadline:	June 13, 2003
Acceptance Notification:	July 4, 2003
Camera-ready Copies:	July 11, 2003
Workshop date:	September 23, 2003

--> Scientific Committee:

Ameen Abu-Hanna, The Netherlands
Riccardo Bellazzi, Italy
Concha Bielza, Spain
Jesús Cerquides, Spain
Gregory F. Cooper, USA
Robert Cowell, USA
Luis de Campos, Spain
Joao Gama, Portugal
David Heckerman, USA
Toni Jebara, USA
Igor Kononenko, Slovenia
Ramon López de Mantaras, Spain
Peter Lucas, The Netherlands
Serafín Moral, Spain
Niels Peek, The Netherlands
Marco Ramoni, USA
Paola Sebastiani, USA
Bo Thiesson, USA
Steven van Dijk, The Netherlands
Linda van der Gaag, The Netherlands
Marco Zaffalon, Switzerland

--> Organization Committee:

Pedro Larrañaga (ccplamup@si.ehu.es)
Intelligent Systems Group. University of the Basque Country. P.O. Box 649. E-20080 Donostia - San Sebastián (Spain).
http://www.sc.ehu.es/ccwbayes/members/pedro.htm

Jose A. Lozano (lozano@si.ehu.es)
Intelligent Systems Group. University of the Basque Country. P.O. Box 649. E-20080 Donostia - San Sebastián (Spain).
http://www.sc.ehu.es/ccwbayes/members/joseantonio.htm

Jose M. Peña (jmp@cs.auc.dk)
Decision Support Systems. Aalborg University. Fredrik Bajers Vej 7E. DK-9220 Aalborg (Denmark).
http://www.cs.auc.dk/~jmp/

Iñaki Inza (inza@si.ehu.es)
Intelligent Systems Group. University of the Basque Country. P.O. Box 649. E-20080 Donostia - San Sebastián (Spain).
http://www.sc.ehu.es/ccwbayes/members/inaki.htm