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2nd International Conference on Complexity, Future Information Systems and...
Mon, Apr 24, 2017, 9:00 AM – Wed, Apr 26, 2017, 6:00 PM WEST
COMPLEXIS – the International Conference on Complexity, Future Information Systems and Risk, aims at becoming a yearly meeting place for presenting and discussing innovative views on all aspects of Complex Information Systems, in different areas such as Informatics, Telecommunications, Computational Intelligence, Biology, Biomedical Engineering and Social Sciences. Information is pervasive in many areas of human activity – perhaps all – and complexity is a characteristic of current Exabyte-sized, highly connected and hyper dimensional, information systems. COMPLEXIS 2017 is expected to provide an overview of the state of the art as well as upcoming trends, and to promote discussion about the potential of new methodologies, technologies and application areas of complex information systems, in the academic and corporate world.
Oleg Gusikhin, Ford Motor Company, United States
Víctor Méndez Muñoz, Universitat Autònoma de Barcelona, UAB, Spain
Farshad Firouzi, IMEC/Katholieke Univ. Leuven, Germany
Dan Mønster, Aarhus University, Denmark
Big Data, Smart Data and Imbalanced Classification: Preprocessing, Models and Challenges
University of Granada
Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has been the supervisor of 40 Ph.D. students. He has published more than 300 journal papers that have received more than 50000 citations (Scholar Google, H-index 112). He is coauthor of the books "Genetic Fuzzy Systems" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015), "The 2-tuple Linguistic Model. Computing with Words in Decision Making" (Springer, 2015), "Multilabel Classification. Problem analysis, metrics and techniques" (Springer, 2016), "Multiple Instance Learning. Foundations and Algorithms" (Springer, 2016). He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as editorial member of a dozen of journals. He received several honors and awards: ECCAI Fellow 2009, IFSA Fellow 2013, 2010 Spanish National Award on Computer Science ARITMEL to the "Spanish Engineer on Computer Science", International Cajastur "Mamdani" Prize for Soft Computing (Fourth Edition, 2010), IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015 respectively), 2011 Lotfi A. Zadeh Prize Best paper Award of the International Fuzzy Systems Association, 2013 AEPIA Award to a scientific career in Artificial Intelligence, and 2014 XV Andalucía Research Prize Maimónides (by the regional government of Andalucía). He has been selected as a 2014 Thomson Reuters Highly Cited Researcher http://highlycited.com/ (in the fields of Computer Science and Engineering, respectively) .
Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. To overcome this issue, the MapReduce framework has arisen as a"de facto" solution. Basically, it carries out a "divide-and-conquer" distributed procedure in a fault-tolerant way to adapt for commodity hardware. Learning with imbalanced data refers to the scenario in which the amounts of instances that represent the concepts in a given problem follow a different distribution. The main issue when addressing such a learning problem is when the accuracy achieved for each class is also different. This situation occurs since the learning process of most classification algorithm is often biased towards the majority class examples, so that minorities ones are not well modeled into the final system. Being a very common scenario in real life applications, the interest of researchers and practitioners on the topic has grown significantly during these years. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts are accentuated during the data partitioning to fit the MapReduce programming style.
In this talk we will pay attention to the imbalanced big data classification problem, we will analyze the current research state of this are, the behavior of standard preprocessing techniques in this particular framework toward, and we will carry out a discussion on the challenges and future directions for the topic.
Natural Computation for the Data Age
Leeds Beckett University
Hissam Tawfik is a Professor of Computer Science with research expertise and interests in the areas of Biologically Inspired Computing, Health Informatics and Technology Acceptance. Hissam has a research track record of more than 100 refereed journal and conference publications and he is a visiting Professor at the University of Seville. Hissam Tawfik holds a PhD in Computer Engineering from the University of Manchester (then UMIST) and has been actively publishing since 1997. His main research areas include Neural and Evolutionary Computing, ICT for Active Ageing, Technology Acceptance and Intelligent Systems and Simulation applications. Some of the projects that Hissam is currently working on include the use of various neural network paradigms for time-series prediction, designing E-Health solutions to support active ageing and people with Dementia, and investigating cultural factors that influence the adoption of E-Health technologies. Hissam has previously worked on a number of EU funded research projects applying Virtual Reality Technology for construction and urban planning and led a British Council funded project on user-centred Health Informatics. Hissam serves on various editorial boards and review committees for international journals and conferences and is a chair and organiser of the International Conference Series on Developments in eSystems Engineering (DESE).
Nature inspired problem solving paradigms have for long attracted the interest of scientists and engineers in their search for robust and sophisticated solutions to real and complex world and problems. In this age of ‘Big’ Data, natural computation techniques such as neural and evolutionary computing techniques is increasingly playing a key role big data inspired applications and solutions. This talk will discuss the opportunities and challenges associated with the use of biologically motivated and other natural computing techniques as a modern data science tool, in this big data era.
Paper Submission: December 15, 2016 (expired)
Authors Notification: February 14, 2017 (extended)
Camera Ready and Registration: February 23, 2017
Authors Notification: February 27, 2017
Camera Ready and Registration: March 13, 2017
NO REFUNDS ON REGISTRATIONS ALLOWED
NO TRANSFER ON REGISTRATIONS ALLOWED
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