CFP: IDA Special Issue on Adaptive Learning Systems

[João Gama]

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			   CALL FOR PAPERS
		Intelligent Data Analysis  - IOS Press

			   SPECIAL ISSUE on

                  INCREMENTAL LEARNING SYSTEMS CAPABLE
		    OF DEALING WITH CONCEPT DRIFT
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Please distribute this announcement to all interested parties.

Special issue Editors:
Miroslav Kubat, University of Miami, USA
Joo Gama, University of Porto, Portugal
Paul Utgoff, University of Massachusetts, USA

Suppose the existence of a concept description that has been induced
from a set, T, of training examples.  Suppose that later another set,
T', of examples become available.  What is the most effective way to
modify the concept so as to reflect the examples from T'?

In many real-world learning problems the data flows continuously and
learning algorithms should be able to respond to this circumstance.
The first requirement of such algorithms is thus incrementality, the
ability to incorporate new information.  If the process is not
strictly stationary, the target concept could gradually change over
time, a fact that should be reflected also by the current version of
the induced concept description.

The ability to react to concept drift can thus be viewed as a natural
extension of incremental learning systems.  These techniques can be
useful for scaling-up learning algorithms to very large datasets.
Other types of problems were these techniques could be potentially
useful include: user-modelling, control in dynamic environments,
web-mining, times series, etc.

Most of evaluation methods for machine learning
(e.g. cross-validation) assume that examples are independent and
identically distributed. This assumption is clear unrealistic in the
presence of concept drift.  How can we estimate the performance of
learning systems under these constrains?

The objective of the special issue is to present the current status of
algorithms, applications, and evaluation methods for these problems.

Relevant techniques include the following (but are not limited to):
1.      Incremental, online, real-time, and any-time learning algorithms
2.      Algorithms that learn in the presence of concept drift
3.      Evaluation Methods for dynamic instance distributions
4.      Real world applications that involve online learning
5.      Theory on learning under concept drift.


Submission Details:
We are expecting full papers to describe original, previously
unpublished research, be written in English, and not be simultaneously
submitted for publication elsewhere (previous publication of partial
results at workshops with informal proceedings is allowed).  We could
also consider the publication of high-quality surveys on these topics.

Please submit a PostScript or PDF file of your paper to:
jgama at liacc.up.pt

Important Dates:
Submission Deadline:  1 of February 2003
Author Notification:  1 of July 2003
Final Paper Deadline: 1 of September 2003
Special Issue:
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