IEEE International Conference on Evolutionary Computation

Donostia – San Sebastián, Spain, June 5-8, 2017

Special Session on Evolutionary methods and machine learning: data preprocessing, learning models and their applications

Session Details:

Title: Evolutionary methods and machine learning: data preprocessing, learning models and their applications.

Organizers: Mikel Galar, Public University of Navarre, Pamplona, Spain; Isaac Triguero, University of Nottingham, United Kingdom; José Maria Luna, University of Cordoba, Spain


Important Dates:

Submission Deadline: 30th January 2017 (final deadline)

Notification Acceptance: 26th February 2017

Paper Submission:

You should follow the IEEE CEC 2017 Submission Web Site. Special session papers are treated the same as regular conference papers.

Brief Description:

The aim of this special session is to serve as a forum for the exchange of ideas and discussions on recent and new trends regarding intersections between evolutionary algorithms and machine learning methods. Machine learning is a very active research field because of the huge number of real-world applications that can be addressed by this field of research. There are many contemporary problems, besides the canonical classification, regression, clustering or pattern mining, which require special focus and development of novel and effective solutions. Such challenges include the problem of imbalanced data, learning on the basis of low quality and noisy examples, multi-label and multi-instance problems, or having limited access to object labels at the training phase, among others.

Learning methods based on Evolutionary techniques are widely used to face the aforementioned challenges with promising results. They can be used either in the data processing part (i.e., data reduction or augmentation such as feature and instance selection or feature engineering) or in the learning process (i.e., genetics-based machine learning, evolving ensembles, neural networks or fuzzy systems). Moreover, Big Data scenario opens up new possibilities for Evolutionary methods in machine learning. New challenges arose with the need of effectively processing large amounts of data in reasonable times.

From this viewpoint, the aim of this special session is to explore Evolutionary methods and machine learning in any part of the learning process both in classical scenarios and in the new directions for addressing Big Data problems. We encourage authors to submit original papers as well as preliminary and promising works in the topics of this special session.

Objectives and topics:

The aim of the session is to provide a forum for the exchange of ideas and discussions on evolutionary algorithms for machine learning, in order to deal with the current challenges in this topic. The special session is therefore open to high quality submissions from researchers working in learning problems using evolutionary techniques. The topics of this special session include evolutionary models for handling data-level difficulties and improving machine learning methods in areas such as:

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 Automatic and Computation at the Public University of Navarre. He is the author of 28 published original articles in international journals and 40 contributions to conferences. He is also reviewer of more than 35 international journals. He received the IEEE Transactions on Fuzzy Systems Outstanding Paper Award 2013 (bestowed in 2016). His research interests are data-mining, classification, big data learning, ensemble learning, evolutionary algorithms and fuzzy systems. He is a member of the European Society for Fuzzy Logic and Technology (EUSFLAT), the Spanish Association of Artificial Intelligence (AEPIA) and the IEEE.

Isaac Triguero received the M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2009 and 2014, respectively. He is currently an Assistant Professor in Data Science at the School of Computer Science of the University of Nottingham. He has published more than 25 international journal papers as well as more than 20 contributions to conferences. His research interests include data mining, data reduction, biometrics, optimization, evolutionary algorithms, semi-supervised learning, bioinformatics and big data learning.

José María Luna received the Ph.D. degree in Computer Science from the University of Granada (Spain) in 2014 and the M.Sc. degree from the University of Córdoba (Spain) in 2009. His pre-doctoral research was granted by the Ministry of Education with FPU AP2010-0041. Recently, he was awarded with a JdC – training PostDoc. He is author of the book "Pattern Mining with Evolutionary Algorithms", published by Springer. He has published close to 20 papers in international journals and more than 25 articles in international scientific conferences. He is also author of two book chapters. Dr. Luna has been engaged in 4 national and regional research projects. He has contributed to 3 international projects. His research is performed as a member of the Knowledge Discovery and Intelligent Systems Laboratory and is focused on evolutionary computation, pattern mining, association rule mining and its applications.

Contact information:

Name: Mikel Galar

Email address:

Affiliation: Public University of Navarra

Postal address: Department of Automatics and Computations, Public University of Navarre, 31006 Pamplona, Spain
Telephone number: +34 948 166040

Name: Isaac Triguero

Email address:

Affiliation: School of Computer Science, University of Nottingham.

Postal address: Jubilee Campus,Wollaton Road, Nottingham NG8 1BB, United Kingdom.

Telephone number: +44(0)115 8466416

Name: José María Luna

Email address:

Affiliation: University of Córdoba.

Postal address: Rabanales Campus, "Albert Einstein" building, 3rd floor. 14071, Córdoba (Spain)

Telephone number: +34 957 212218