Connectionists: IEEE TNNLS Call for Special Issue on "Stream Learning", Submission Deadline: December 15, 2021 [EXTENDED]

[Pub Conference]

*CALL FOR PAPERS*

*IEEE Transactions on Neural Networks and Learning Systems Special Issue on
STREAM LEARNING*


https://cis.ieee.org/images/files/Publications/TNNLS/special-issues/One-Page_IEEE_Transactions_on_NNLS-SI-CFP-Update.pdf

*Introduction*

In recent years, machine learning from streaming data (called *Stream
Learning*) has enjoyed tremendous growth and exhibited a wealth of
development at both the conceptual and application levels. Stream Learning
is highly visible in both the machine learning and data science fields, and
become a new hot direction in recent years. Research developments in Stream
Learning include learning under concept drift detection (whether a drift
occurs), understanding (where, when, and how a drift occurs), and
adaptation (to actively or passively update models). Recently we have seen
several new successful developments in Stream Learning such as massive
stream learning algorithms; incremental and online learning for streaming
data; and streaming data- based decision-making methods. These developments
have demonstrated how Stream Learning technologies can contribute to the
implementation of machine learning capability in dynamic systems. We have
also witnessed compelling evidence of successful investigations on the use
of Stream Learning to support business real-time prediction and decision
making.

In light of these observations, it is instructive, vital, and timely to
offer a unified view of the current trends and form a broad forum for the
fundamental and applied research as well as the practical development of
Stream Learning for improving machine learning, data science and practical
decision support systems of business. This special issue aims at reporting
the progress in fundamental principles; practical methodologies; efficient
implementations; and applications of Stream Learning methods and related
applications. The special issue also welcomes contributions in relation to
data streams, incremental learning and reinforcement learning in data
streaming situations.
Scope of the Special Issue

We invite submissions on all topics of Stream Learning, including but not
limited to:

•  *Data stream prediction*

•  *Concept drift detection, understanding and adaptation*

•  *Recurrent concepts*

•  *Experimental setup and Evaluation methods for stream learning*

•  *Reinforcement learning on streaming data*

•  *Streaming data-based real-time decision making*

•  *Ensemble methods for stream learning*

•  *Auto machine learning for stream algorithms*

•  *Neural networks for big data streams*

•  *Transfer learning for streaming data*

•  *Real-world applications of stream learning*

•         *Active learning for streaming data*

•         *Online learning for streaming data*

•         *Imbalance learning for streaming data*

•         *Lifelong learning for streaming data*

•         *Incremental learning for streaming data*

•         *Continuous learning for streaming data*

•         *Clustering for streaming data*

•         *Audio/speech/music streams processing*

•         *Stream learning benchmark datasets*

•         *Multi-drift and multi-stream learning*

•         *Stream processing platforms*
Timeline

•  Submission deadline: *Dec 15, 2021*

•  Notification of first review: March 1, 2022

•  Submission of revised manuscript: Jun 1, 2022

•  Notification of final decision: Aug 1, 2022
Guest Editors

•           Jie Lu (University of Technology Sydney, Australia)

•           Joao Gama (University of Porto, Portugal)

•           Xin Yao (Southern University of Science and Technology, China)

•           Leandro Minku (University of Birmingham, UK)
Submission Instructions

-   Read the Information for Authors at http://cis.ieee.org/tnnls
<http://cis.ieee.org/tnnls>

-    Submit your manuscript at the TNNLS webpage (http://mc.manuscriptcentr
<http://mc.manuscriptcentral.com/tnnls)>al.com/tnnls)
<http://mc.manuscriptcentral.com/tnnls)> and follow the submission
procedure. Please, clearly indicate on the first page of the manuscript and
in the cover letter that the manuscript is submitted to this special issue.
Early submissions are welcome.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20211027/a1121510/attachment.html>