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HEMLCDD 2015 : New Generation Computing Special Issue on Hybrid Ensemble Machine Learning for Complex and Dynamic Data

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Link: http://kms.ii.pwr.wroc.pl/pl/events/hemlcdd2014
 
When Dec 15, 2014 - Dec 15, 2014
Where N/A
Submission Deadline Dec 15, 2014
Categories    machine learning   pattern recognition   artificial intelligence   big data
 

Call For Papers

The New Generation Computing Journal welcomes contributions for a special issue:

Hybrid Ensemble Machine Learning for Complex and Dynamic Data


Objectives and topics:

Hybrid and ensemble methods in machine learning have gained a great attention of scientific community over the last several years. Multiple learning models have been theoretically and empirically shown to provide significantly better performance than their single base models. Their most interesting application area lies in analyzing of complex, high dimensional and big data, that cannot be handle efficiently by single -model approaches. Another contemporary problem lies in providing efficient compound methods for tackling streams of data in dynamic and non-stationary environments. This Special Issue of New Generation Computing, is devoted to both hybrid and ensemble methods in solving complex and non-stationary problems. We want to offer an exciting opportunity for researchers and practitioners to present their work and publish recent advances in this area.

The scope of the special issue includes the following topics:

Theoretical framework for ensemble methods
Ensemble learning algorithms: bagging, boosting, stacking, etc.
Hybridization of ensembles
Combined classifiers for big and high-dimensional data
Multiple Classifier Systems for im balanced classification
Ensembles for one-class classification
Mining data streams using ensemble methods
Ensemble methods for dealing with concept drift
Incremental, evolving, and online ensemble learning
Diversity, accuracy, interpretability, and stability issues
Classifier selection and ensemble pruning
Subsampling and feature selection in multiple model machine learning
Multi-objective ensemble learning
Assessment and statistical analysis of ensemble models
Applications of ensemble methods in business, engineering, medicine, etc.


Important dates:

Submission of the paper for review via EasyChair: December 15, 2014
First round of reviews: January 15, 2015
Revised version submission deadline: February 15, 2015
First round of reviews: March 15, 2015
Camera-ready copies of accepted papers due: April 15, 2015



Guest editors:
Bartosz Krawczyk, Wroclaw University of Technology, Poland bartosz.krawczyk@pwr.edu.pl
Bogdan TrawiƄski, Wroclaw University of Technology, Poland bogdan.trawinski@pwr.edu.pl



Authors are encouraged to send new, unpublished research results. However, in special cases extended works based on previously published conference papers can be considered. However, the journal submission must contain at least 50% new material and the title of the extended version must clearly and unmistakably differ from the title of the article presented at the conference.

The submission of the paper for the revision should be send in the electronic version (PDF) via EasyChair available at https://www.easychair.org/conferences/?conf=hemlcdd2014. To be fully considered for publication, papers must be received by the due date and meet the following requirements. Papers must be written in English and the maximal length of the final version should be 20 pages (incl. figures and tables) in the journal format. The electronic data of the final version of papers must be prepared in LaTeX according to the New Generation Computing guidelines.

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