posted by organizer: lamirel || 6435 views || tracked by 1 users: [display]

EvoLearn-BF 2019 : Workshop EvoLearn-BF - Evolutionary classification and clustering, concept drift, novelty detection in big/fast data context


When Jun 10, 2019 - Jun 10, 2019
Where Wellingtion
Submission Deadline May 1, 2019
Notification Due May 15, 2019
Final Version Due May 30, 2019
Categories    machine learning   incremental   artificial intelligence   statistics

Call For Papers

Workshop EvoLearn-BF - Evolutionary classification and clustering, concept drift, novelty detection in big/fast data context

Wellington, New Zealand,
June 10, 2019

in conjunction with

CEC 2019
2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 10-13, 2019


This workshop aims to offer a meeting opportunity for academics and industry-related researchers,
belonging to the various communities of Computational Intelligence, Machine Learning, Experimental Design,
Data Mining and Big/Fast Data Management to discuss new areas of incremental classification,
concept drift management and novelty detection and on their application to analysis of time varying information
and huge dataset of various natures.
Another important aim of the workshop is to bridge the gap between data acquisition or experimentation
and model building.

Topics of interest to the workshop include (but are not limited to):

• Novelty detection algorithms and techniques
• Semi-supervised and active learning approaches
• Machine learning for data streams
• Adaptive hierarchical, k-means or density based methods
• Adaptive neural methods and associated Hebbian learning techniques
• Multiview diachronic approaches
• Probabilistic approaches
• Distributed approaches
• Graph partitioning methods and incremental clustering approaches based on attributed graphs
• Incremental clustering approaches based on swarm intelligence and genetic algorithms
• Evolving classifier ensemble techniques
• Incremental classification methods and incremental classifier evaluation
• Dynamic variable selection techniques
• Clustering of time series
• Visualization methods for evolving data analysis results

The list of application domain is includes, but it is not limited to:

• Evolving textual information analysis
• Evolving social network analysis
• Dynamic process control and tracking
• Intrusion and anomaly detection
• Genomics and DNA microarray data analysis
• Adaptive recommender and filtering systems
• Scientometrics, webometrics and technological survey


• Jean-Charles Lamirel, SYNALP-LORIA, Campus Scientifique, Vandoeuvre les Nancy, France
• Pascal Cuxac, INIST-CNRS, 2 allee du Parc de Brabois, Vandoeuvre les Nancy, France
• Mustapha Lebbah, Laboratoire d'Informatique de Paris-Nord (LIPN), Paris 13 University, Villetaneuse, France

Invited speaker:

• Albert Bifet, Télécom-ParisTech - France / University of Waikato - New Zealand

Albert Bifet is Professor at Data, Intelligence and Graphs (DIG) LTCI, Télécom ParisTech (France)
and University of Waikato (New Zealand).
His research focuses on Artificial Intelligence, Big Data Science, and Machine Learning for
Data Streams.

Albert Bifet is co-author of the book “Machine Learning for Data Streams” of MIT Press
and co-leading the open source projects MOA Massive On-line Analysis and Apache SAMOA
Scalable Advanced Massive Online Analysis.

Important Dates:

• Paper submission: May 1, 2019
• Notification of acceptance: May 15, 2019
• Camera-ready: May 30, 2019
• Conference: June 10, 2019

Submission instructions:

The objective of this workshop is to facilitate presentations and discussions to share experience
and knowledge on the issues related to incremental learning.

May be submitted:

• Extended summaries (4 pages)
• Long articles (maximum 12 pages)
• Software demonstration proposals (4 pages)

Many kinds of submissions are also welcome :

• Academic contributions
• Contributions on the practical relevance of research work or models, whether from industry or academia,
or with collaborations between the two
• Submission of well-articulated positions, industrial experiences and ongoing work

For demonstrations: an arranged oral presentation should be prepared.
Time will also be provided in the program for demos.

Submission format should be compliant with the one of the CEC 2019 conference :

Reviewing for CEC-EvoLearn 2019 will be blind: reviewers will not be presented with the identity of
paper authors. To allow for blind review, author names in a submitted paper should be replaced by
the unique tracking number assigned by the conference website at the submission of an electronic abstract.
Authors should avoid writing anything that makes their identity obvious in the text.

Please use submission link :

After the workshop, if the quantity and quality of submissions justifies a special journal issue,
authors of selected papers will be invited to re-submit their work to be considered for inclusion
in a special issue of a journal.

Related Resources

MLDM 2023   18th International Conference on Machine Learning and Data Mining
JCICE 2024   2024 International Joint Conference on Information and Communication Engineering(JCICE 2024)
ICDM IncrLearn 2023   Workshop on Incremental classification and clustering, concept drift, novelty detection, active learning in big/fast data context
ICDM 2024   24th Industrial Conference on Data Mining
GECCO 2024   Genetic and Evolutionary Computation Conference
ACM-Ei/Scopus-CCISS 2024   2024 International Conference on Computing, Information Science and System (CCISS 2024)
ICAISC 2024   International Conference on Artificial Intelligence and Soft Computing
ICANN 2024   33rd International Conference on Artificial Neural Networks
GECCO 2024   The Genetic and Evolutionary Computation Conference (GECCO 2024)
IEA/AIE 2024   37th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems