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Evolving Systems 2015 : Special Issue on Data Stream Mining and Applications

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Link: http://www.springer.com/cda/content/document/cda_downloaddocument/CallForPapers_DataStreamMining.pdf?SGWID=0-0-45-1503568-p173913237
 
When N/A
Where N/A
Submission Deadline Aug 31, 2015
Notification Due Nov 30, 2015
Final Version Due Feb 29, 2016
 

Call For Papers

A.Scope

In current industrial systems, the necessity of data stream mining and learning from data streams is increasingly becoming more prevalent and urgent, due to speed, volume and on-line nature of the data generated by such systems. While conventional batch and off-line training approaches provide a possible solution, such approaches are often too time and memory intensive, and cannot process the data at the high enough rate that is often desired. This is true even when batch and off-line approaches are applied to sliding windows or onto streaming samples gathered from reservoir computing techniques.

An important aspect in data stream mining is that the data analysis system, the learner, has no control over the order of samples that arrive over time --- they simply arrive in the same order they are acquired and recorded. Also, the learning algorithms usually have to be fast enough in order to cope with (near) real-time and on-line demands. This usually requires a single-pass learning procedure, restricting the algorithm to update models and statistical information in a sample-wise manner, without using any prior data (or at least to aim for using as little prior data as possible). In literature, this is also termed as incremental or sequential learning and plays a key role in data stream mining frameworks and environments.

In some cases, it is sufficient to only update (some of) the model parameters, whereas in other cases the evolution of new structural components (e.g. adding a new neuron, rule or leaf) may be necessary in order to expand models on-the-fly and on demand to new operating modes, dynamically changing states or non-stationary environmental conditions.


B.Topics

All contributions relevant to learning from data streams, in particular those associated with non-stationary or changing distributions are welcome! We provide a list of a few core themes as valuable examples for the special issue:

1.Data Stream Mining with Soft Computing Techniques such as (but not nec. restricted to):
o Evolving fuzzy systems (EFS) and classifiers (EFC)
o Sequential radial basis functions networks
o Sequential multilayer perceptron
o Online probabilistic neural networks
o Evolving bio-inspired approaches
o On-line genetic-based modelling systems and dynamic evolutionary algorithms
o Any form of online, evolving hybrid (e.g. neuro-fuzzy, neuro-genetic) approaches

2.Data Stream Mining with Machine Learning and Data Mining Concepts such as (but not necessarily restr. to):
o Online and incremental support vector machines
o Evolving self-organizing maps
o Incremental decision trees
o Incremental ensemble classifier and trained fusion techniques
o Bagging and Boosting in data streams
o Evolving cluster models and incremental unsupervised learning
o Incremental statistical learning techniques in non-stationary environments
o Incremental Kernel-based learning and density ratio matching

3.Advanced Aspects for Improved Stability and Useability (but not necessarily restr. to):
o Concepts to address drifts and shifts in Data Streams
o Concepts to address domain adaptation
o Concepts to address importance weighting and sampling
o On-line single-pass active learning from Data Streams
o Semi-supervised learning from Data Streams
o Fast adaptive, incremental learning methods (“hazard” form)
o Dynamic dimension reduction and feature selection in Data Streams
o Reliability in model predictions and parameters
o Hybrid modelling aspects (refining knowledge-based models with data)
o Parameter-low and –insensitive learning methods
o On-line complexity reduction to emphasize transparent, more compact models
o Concepts to address linguistic interpretability
o Concepts to address visual interpretability (model development over time)
o Online tuning via human-machine interaction

4.Real-World Applications of data stream mining such as (but not necessarily restricted to):
o Data stream modelling and identification (supervised and unsupervised)
o Big Data handled in a streaming concept
o Online fault detection and decision support systems
o Online media stream classification
o Process control and condition monitoring
o Modeling in high throughput production systems
o Web applications
o Adaptive chemometric models in dynamic chemical processes
o Online time series analysis and stock market forecasting
o Robotics, Intelligent Transport and Advanced Manufacturing
o Adaptive Evolving Controller Design
o User Activities Recognition
o Cloud Computing
o Multiple Sensor Networks


C.Important dates
o Submission deadline: 31st of August, 2015
o First author notification: 30th of November, 2015
o Revised version: 15th of January, 2016
o Final notification: 29th of February, 2016
o Publication: Spring 2016


The papers should be formatted according the instruction guidelines for authors found at http://www.springer.com/physics/complexity/journal/12530 and submitted through the regular submission gate at @ Evolving Systems journal (Springer) located http://www.springer.com/physics/complexity/journal/12530 by choosing S.I.: Data Stream Mining and Applications as article type.


Co-Organizers
Edwin Lughofer, University of Linz, Austria, edwin.lughofer@jku.at
Robi Polikar, Rowan University, Glassboro, New Jersey, United States, polikar@rowan.edu
Mu-Yen Chen, Department of Information Management, National Taichung University of Science and Technology, Taiwan, E-mail: mychen@nutc.edu.tw

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