Big Data, Smart Data and Imbalanced Classification - Preprocessing, Models and Challenges
Francisco Herrera, University of Granada, Spain
Autonomy Requirements for Smart Vehicles
Emil Vassev, Lero - The Irish Software Research Centre, UL, Limerick, Ireland
Big Data, Smart Data and Imbalanced Classification - Preprocessing, Models and Challenges
Francisco Herrera
University of Granada
Spain
http://decsai.ugr.es/~herrera
Brief Bio
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 42 Ph.D. students. He has published more than 400 journal papers that have received more than 62000 citations (Scholar Google, H-index 125). 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 the following 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), 2017 Security Forum I+D+I Prize, and 2017 Andalucía Medal (by the regional government of Andalucía). He has been selected as a Highly Cited Researcher http://highlycited.com/ (in the fields of Computer Science and Engineering, respectively, 2014 to present, Clarivate Analytics). His current research interests include among others, soft computing (including fuzzy modeling, evolutionary algorithms and deep learning), computing with words, information fusion and decision making, and data science (including data preprocessing, prediction and big data).
Abstract
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.
Autonomy Requirements for Smart Vehicles
Emil Vassev
Lero - The Irish Software Research Centre, UL, Limerick
Ireland
www.vassev.com
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Brief Bio
Dr. Emil Vassev received his M.Sc. in Computer Science (2005) and his PhD in Computer Science (2008) from Concordia University, Montreal, Canada. Currently, he is a Senior Research Fellow at Lero-the Irish Software Research Centre, University of Limerick, Ireland where he has led and is currently leading a few important projects including projects with the European Space Agency. Dr. Vassev's research focuses on knowledge representation and awareness for self-adaptive systems. A part from the main research, his research interests include engineering autonomic systems, compilers (including llvm), distributed computing, formal methods, cyber-physical systems and software engineering. He has published three books and over 140 internationally peer-reviewed papers, including the recently published book on "Autonomy Requirements and Engineering for Space Missions". As part of his collaboration with NASA, Vassev has been awarded two patents.
Abstract
In one aspect of our life or another, today we all live with AI. For example, the mechanisms behind the search engines operating on the Internet do not just retrieve information, but also constantly learn how to respond more rapidly and usefully to our requests. Basically, AI depends on our ability to efficiently transfer knowledge to software-intensive systems. A computerized machine can be considered as one exhibiting AI when it has the basic capabilities to transfer data into context-relevant information and then that information into conclusions exhibiting knowledge.
Closely related to AI, autonomous systems not only exhibit knowledge but also autonomously interact with their operational environment and perceive important structural and dynamic aspects of the same. The underlying mechanism for this autonomy helps such systems monitor, draw inferences and react. The integration and promotion of autonomy in software-intensive systems is an extremely challenging task. Among the many challenges software engineers must overcome are those related to elicitation and expression of autonomy requirements.
In this talk, the speaker will draw upon his experience with the Autonomy Requirements Engineering (ARE) approach to present how ARE handles autonomy requirements for smart vehicles. The emphasis will be put on using ARE to extend upstream control software with special self-managing objectives (self-* objectives) intended to provide an ability to autonomously and automatically discover, diagnose, and cope with various problems that need to be overcome during car operation.