C A L L F O R P A P E R S
-----------------------------
>>>>>>> Pre- and Post-Processing in Machine Learning and Data Mining: <<<<<<<
>>>>>>> Theoretical Aspects and Applications <<<<<<<
:::::A WORKSHOP WITHIN::::
MACHINE LEARNING AND APPLICATIONS
Advanced Course on Artificial Intelligence 1999 (ACAI-99)
5-16 July 1999, Greece
(http://www.iit.demokritos.gr/skel/eetn/acai99)
European Coordinating Committee on Artificial Intelligence (ECCAI)
(http://www.eccai.org)
&
Hellenic Artificial Intelligence Society (EETN)
(http://www.iit.demokritos.gr/skel/eetn)
This workshop addresses an important aspect related to the
Data Mining (DM) and Machine Learning (ML) in pre-processing and analyzing
real-world data.
First, the data which are to be processed by a DM
algorithm are usually noisy and often inconsistent. Many steps are
involved before the actual data analysis starts. Moreover, the
genuine logical ML systems do not easily allow processing of numerical
attributes as well as numerical (continuous) classes. Therefore, certain
procedures have to precede the actual data analysis process.
Second, a result of a genuine ML algorithm, such as a decision
tree or a set of decision rules, need not be perfect from the view of
custom or commercial applications. It is quite known that a concept
description as a result of an inductive process has to be usually
processed by a pre- or post-pruning procedure. Other post-processing
procedures include rule quality processing, rule filtering, rule combination,
or even knowledge integration. All these procedures provide a
kind of "symbolic filter" for noisy, imprecise, or "non-user-friendly"
knowledge derived by an inductive algorithm.
Thus, the pre- and post-processing tools always help the DM
algorithms to investigate databases as well as to refine the acquired
knowledge. Usually, these tools exploit techniques that are not
genuinely logical, e.g., statistics, neural nets, and others.
These reasons let us to launch this workshop. In fact, we would
like to support both theoretical aspects of this issue and practical,
experienced, empirical applications. As for the latter, we would like to focus
on industry and business applications, but we will review any functional
application of the above concern in any discipline.
The theme of this workshop is directly related to the "Machine Learning
and Applications" conference:
1. It provides a forum for researchers and practitioners who are interested
in the scope of the workshop to exchange information by attending and/or
presenting a paper.
2. Since this workshop is focused on both theoretical and applications
aspects, it provides an opportunity for researchers to learn about the
challenges and real problems in development and applications of machine
learning techniques.
Organizers:
----------
A. (Fazel) Famili (chair)
Editor-in-Chief, Intelligent Data Analysis http://www.elsevier.com/locate/ida
Phone: +1-613-9938554
Institute for Information Technology Fax : +1-613-9527151
National Research Council of Canada, Email: Fazel.Famili@ai.iit.nrc.ca
Bldg. M-50, Montreal Rd. Ottawa, Ont. http://ai.iit.nrc.ca/~fazel
Canada K1A 0R6
Ivan Bruha (contact person)
^^^^^^^^^^^^^^^^
http://www.cas.mcmaster.ca/~bruha
McMaster University Phone: +1-905-5259140 ext 23439
Dept. Computing & Software Fax: +1-905-5240340
Hamilton, Ont. Email: bruha@mcmaster.ca
Canada L8S 4L7
Organization Notes:
------------------
There will be one or two invited talks on the workshop which will
survey the given topic as well as introduce their own research.
Up to 10 accepted papers will be presented (each 15-20 min). If there
is a larger interest, then some papers might be accepted as posters.
Maximum size is 10 (ten) pages. Submit your paper either by regular mail or by
Email to I. Bruha. If you use Email, then the PostScript format would be the
^^^^^^^^
most suitable.
The workshop will take place during the afternoon sessions of ACAI-99,
from 14:30 to 18:00. Depending on the number of accepted papers the workshop
will take place one or two afternoon sessions. The exact date of the workshop
will be finalized by the ACAI-99 organizing committee.
Anyone registered for the main ACAI-99 event can also attend all the
workshops. For the workshop participants who will not be registered for the
whole ACAI-99 there will be a fee that will be finalized soon.
Proceedings will be published as a technical report in collaboration
with the ACAI-99 organization committee.
>>>>>> Please note that authors of the best papers will be invited to <<<<<<<
submit an extended version of their papers to the Intelligent Data Analysis
Journal (http://www.elsevier.com/locate/ida), or even a special issue of the
journal regarding this topic might be published.
New deadline for paper submission:
---------------------------------
Updated schedule:
Deadline for paper submission: 20 March 1999
Notification of acceptance: 1 April 1999
Deadline for final camera ready papers: 15 April 1999