Special Session for IEEE CEC 2015.
Organizers:Mikel Galar, Isaac Triguero
Brief Description:At this time, big data applications are becoming the main focus of attention because of the huge increase in data generation and storage that has occurred in recent years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because data mining techniques are not adapted to the new requirements of space and time.
Learning methods based on Computational Intelligence techniques are widely used in different fields and the mentioned scalability requirements are a necessity to use them with huge databases.
Evolutionary algorithms constitute a robust technique in complex optimization, identification, learning, and adaptation problems, and they can adapt the model parameters to get a high performance system. However, evolutionary computation methods needs to be adapted and designed to work with Big Data problems as they were not considered such a large quantity of data before
Objectives and topics:The aim of the session is to provide a forum to disseminate and discuss recent and significant research efforts on Evolutionary Computation algorithms for big data, in order to deal with current challenges on this topic. The session is therefore open to any high quality submission from researchers working at the particular intersection of evolutionary computation and big data. The topics of this special session include evolutionary algorithms in the context of big data for addressing the following problems:
- Semi-supervised learning
- Association Mining
- Data Reduction
- Feature Selection / Extraction / Construction
- Instance Selection / Generation
- Rule and Tree Induction
- Statistical Learning and Modelling
- Lazy Learning
- Kernels and Support Vector Machines
- Ensemble learning
- Manifold Learning
- Pre-processing / Post-processing tasks
- One-class classification
- Imbalanced learning
- Bioinformatics applications
- Real World Applications
- Architectures for big data evolutionary learning
Useful datasets that can be used:We encourage to the authors to apply their techniques to very big data sets. Here we provide several links to big data sets that may be used for different learning tasks:
|Bag of Words||Clustering||8000000||100000||Download|
|Electric Power||Regression Clustering||2075259||9||Download|
Short biography of the organizers:Mikel Galar received the M.Sc. and Ph.D. degrees in Computer Science in 2009 and 2012, both from the Public University of Navarre, Pamplona, Spain. He is currently an assistant professor in the Department of Automatics and Computation at the Public University of Navarre. He is the author of 18 published original articles in international journals and 26 contributions to conferences. He is also reviewer of more than 30 international journals. His research interests are data-mining, classification, multi-classification, ensemble learning, evolutionary algorithms, fuzzy systems and fingerprint recognition. He is a member of the European Society for Fuzzy Logic and Technology (EUSFLAT) and the Spanish Association of Artificial Intelligence (AEPIA).
Isaac Triguero received the M.Sc. and Ph.D. degree in Computer Science from the University of Granada, Granada, Spain, in 2009 and 2014, respectively. He is currently post-doctoral researcher at the Inflammation Research Center of the Ghent University, Ghent, Belgium. He has published 16 international journal papers as well as more than 15 contributions to conferences. His research interests include data mining, data reduction, biometrics, evolutionary algorithms, semi-supervised learning and big data learning.
Contact information:Name: Mikel Galar
Email address: firstname.lastname@example.org
Affiliation: Public University of Navarre
Postal address: Department of Automatics and Computations, Public University of Navarre, 31006 Pamplona, Spain Telephone number: +34-948-166040
Name: Isaac Triguero
Email address: Isaac.Triguero@irc.vib-UGent.be
Affiliation: Inflammation Research Center, a VIB-UGent Department UGent Department of Internal Medicine, Respiratory Medicine (GE01)
Postal address: Technologiepark 927, B-9052 Zwijnaarde, Belgium.
Telephone number: +32(0)9 331 37 45