Dear Colleagues and Friends,

We are now organizing a special issue on "Data Stream Mining and Soft Computing Applications" @ Applied Soft Computing (SCI Index) and would like to invite you personally to contribute.

Please find the below call for papers (CFP) and website address,

https://www.journals.elsevier.com/applied-soft-computing/call-for-papers/special-issue-on-data-stream-mining-and-soft-computing-appli


In addition, Authors please choose the Special Issue (SI: Online Streams) on the online submission system (http://ees.elsevier.com/asoc/).

looking forward to your contributions

very best regards

on behalf of the guest editors

Mu-Yen Chen, Ph.D.
Edwin David Lughofer, Ph.D.

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This special issue intends to draw a picture of the recent advances in data stream mining techniques including all incremental machine learning concepts and evolving soft computing modeling strategies for addressing these important problems discussed above. Finally, all emerging and grand-challenge problems, topics such as interpretability aspects in evolving models, and mimicking intelligent brain – even if at a limited scale - are of particular interest to this special issue. Computational aspects such as real-time capability of the learning methods play central roles within all these issues.

Advanced Aspects for Improved Stability, Performance and Usability


•New Algorithms, Concepts in Data Stream Mining with Soft Computing Techniques (for supervised regression, classification and unsupervised learning)
•New Algorithms, Concepts in Mining with Machine Learning Concepts(for supervised regression, classification and unsupervised learning)
•Concepts to address drifts and shifts in Data Streams
•On-line single-pass active learning from Data Streams
•Semi-supervised learning from Data Streams
•Dynamic dimension reduction and feature selection in Streams
•Reliability in model predictions and parameters
•Stability, process-safety and computational related aspects
•Concepts to address linguistic interpretability
•Concepts to address visual interpretability (model development over time)
•Online tuning via human-machine interaction
•Complexity reduction and interpretability issues in evolving models
•Incremental and evolving methods for multi-label classification problems
•On-line ensembling and fusioning methods for improved model output robustness
•Concepts to address dynamic splitting of model components on the fly


Concepts to address dynamic splitting of model components on the fly

Real-World Applications of evolving soft computing techniques

•Data stream modelling and identification
•Online fault detection and decision support systems
•Online media stream classification
•Process control and condition monitoring
•Modeling in high throughput production systems
•Web applications
•Adaptive chemometric models in dynamic chemical processes
•Online time series analysis and stock market forecasting
•Robotics, Intelligent Transport and Advanced Manufacturing
•Adaptive Evolving Controller Design
•User Activities Recognition
•Cloud Computing
•Multiple Sensor Networks
•Big Data


Submission deadline: 31th of January, 2017
First author notification: 30th of April, 2017
Revised version: 30th of June, 2017
Final notification: 31st of August, 2017
Publication: TBD

Mu-Yen Chen, Professor
Department of Information Management,
National Taichung University of Science and Technology, Taichung, 404, Taiwan
E-mail: mychen.academy@gmail.com