Intelligent Data Analysis

Call for Papers

Special Issue On

Incremental Learning Systems

capable of dealing with Concept Drift

Miroslav Kubat, João Gama, and Paul Utgoff

Guest Editors

MOTIVATION AND RESEARCH ISSUES

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 (as is the case in many realistic applications), 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 data streams.
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We are expecting full papers to describe original, previously unpublished research, be written in English, and not be simultaneously submitted for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.

SUBMISSION INFORMATION:

We kindly ask that all submissions be made electronically, as a ps or pdf attachment to: jgama@liacc.up.pt

Since this is a special issue, please be sure also to:

The subject of your email should refer that your submission is for this special issue of IDA.

The body of your email should contain (plain text) the Title, a list of Authors, and the Abstract.

Be sure your cover letter clearly identifies your submission as being made to the special issue.
All queries regarding this special issue should be directed to João Gama (jgama@liacc.up.pt).

Important Dates:

Submission Deadline: 28 February, 2003 (passed)
Author Notification: July 1, 2003 (passed)
Final Paper Deadline: September 1, 2003 (passed)
Special Issue: planned for IDA Volume 8(3) (around May 2004).

REVIEWING PROCESS
The submissions will be evaluated at least by two reviewers.