IEEE International Conference on Evolutionary Computation
Rio de Janeiro, Brazil, July 8-13, 2018
Special Session on Evolutionary Evolutionary Methods in Real-world Machine Learning: Non-standard problems, Big data and Applications
Title: Evolutionary Methods in Real-world Machine Learning: Non-standard problems, Big data and Applications.
Organizers: Mikel Galar, Public University of Navarre, Pamplona, Spain; Isaac Triguero, University of Nottingham, United Kingdom; José Maria Luna, University of Jaen, Spain
Contact: email@example.com, Isaac.Triguero@nottingham.ac.uk, firstname.lastname@example.org.
Submission Deadline: 15th January 2018
Notification Acceptance: 15th March 2018
You should follow the IEEE CEC 2018 Submission Web Site. Special session papers are treated the same as regular conference papers.
The aim of this special session is to serve as a forum for the exchange of ideas and discussions on recent and new trends in complex data mining and machine learning problems with evolutionary 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 non-standard 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 big data, data streams, class-imbalance 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.
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 to address non-standard problems. Real-world applications dealing with non-standard problems with evolutionary methods are also welcomed. 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 dealing with non-standard machine learning problems, handling data-level difficulties and improving machine learning methods in areas such as:
Big data mining
Data streams and concept drift
Supervised / Unsupervised / Semi-supervised learning
Feature Selection / Extraction / Construction
Instance Selection / Generation
Multi-label \ Multi-instance learning
Feature and label noise
Kernels and Support Vector Machines
Evolutionary fuzzy systems
One-class classification / Learning from positive and unlabeled samples
Real-world applications e.g., in medical informatics, bioinformatics, social networks, biometry, etc.
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 Navarra, Pamplona, Spain. He is currently an assistant professor at the Department of Automatic and Computation at the Public University of Navarre. He is the author of 32 published original articles in international journals and more than 45 contributions to conferences. He is also reviewer of more than 35 international journals. His research interests are data mining, classification, multi-classification, ensemble learning, evolutionary algorithms, fuzzy systems and big data. He is a member of the IEEE, the European Society for Fuzzy Logic and Technology (EUSFLAT) and the Spanish Association of Artificial Intelligence (AEPIA). He has received the extraordinary prize for his PhD thesis from the Public University of Navarre and the 2013 IEEE Transactions on Fuzzy System Outstanding Paper Award for the paper “A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications” (bestowed in 2016).
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 30 international journal papers as well as more than 20 contributions to conferences. He is also a reviewer of more than 30 international journals. He has acted as Program Co-Chair of the IEEE Conference on Smart Data (2016), and the IEEE Conference on Big Data Science and Engineering (2017), and he is currently Program Co-Chair of the IEEE BigData Congress (2018). 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. He has currently a post-doctoral position at the University of Jaen, granted by the Spanish Ministry (JdC – training PostDoc). He is author of the book "Pattern Mining with Evolutionary Algorithms", published by Springer in 2016. He has published 24 international journal papers and more than 25 articles in international scientific conferences. Dr. Luna has been engaged in 4 national and regional research projects, and he has also contributed to 3 international projects (Technical University of Eindhoven). His research interests include pattern mining, association rules, exceptional models, subgroup discovery and any other form of supervised descriptive patterns.
Name: Mikel Galar
Email address: email@example.com
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: Isaac.Triguero@nottingham.ac.uk
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: firstname.lastname@example.org
Affiliation: University of Jaen.
Postal address: Las Lagunillas Campus, A3 building, Office 242, 23071, Jaen (Spain)
Telephone number: +34 953212802