Plenary presentation 1: Francisco Herrera
Title: Designing Fuzzy Systems for Big Data - Challenges and Opportunities
Abstract : In this talk we will pay attention to the big data classification problem. We will analyze the available learning algorithms, and we will focus the attention on the fuzzy rule based systems approaches. We will discuss the Map and Reduce phases for designing fuzzy systems for big data, showing some cases of study. We will discuss the challenges and opportunities in the topic.
Francisco Herrera 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 38 Ph.D. students. He has published more than 300 papers in international journals. He is coauthor of the books: “Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases" (World Scientific, 2001) and "Data Preprocessing in Data Mining" (Springer, 2015).
He currently acts as Editor in Chief of the international journals "Information Fusion" (Elsevier) and “Progress in Artificial Intelligence (Springer). He acts as editorial board member or associate editor of a dozen of journals.
He has been given many awards and honors for his personal work or for his publications in journals and conferences, among others, ECCAI Fellow 2009, IFSA Fellow 2013, IEEE Transactions on Fuzzy System Outstanding 2008 and 2012 Paper Award (bestowed in 2011 and 2015), 2011 Lotfi A. Zadeh Prize Best paper IFSA Award.
He belongs to the list of the Highly Cited Researchers in the areas of Engineering and Computer Sciences: http://highlycited.com/ (Thomson Reuters). His h-index is 103 in Scholar Google, receiving more than 40000 citations.
His current research interests include among others, soft computing (including fuzzy modeling and evolutionary algorithms), information fusion, decision making, bibliometrics, biometric, data preprocessing, data science and big data.
lenary presentation 2: Christian Jacquemin
Title: Arts and Science - Illustrative Examples, Critical Analysis, and Future Opportunities
Abstract: Arts and Science has become quite fashionable in recent years... but what is it really? How large is the spectrum of activities claiming to belong to this field? Is it just an ephemeral fashion, or a long-term domain of research and creation? Through illustrative examples taken from our own practice in this domain, or from other representative studies, we will outline what makes an art-science project a very specific type of joint academic/artistic activity. In a second step, we will try to highlight some of the main features of such a research, and propose some tracks for a critical analysis of this domain. Last, considering the multidisciplinary audience of the FLINS conference, we will share our prospects for some of the interesting development opportunities in arts and science.
Christian Jacquemin is a Professor in Computer Science at the University of Paris-Sud since 2000.
His current research interests include interactive 3D graphics and audio, advanced graphics rendering, image analysis for Augmented Reality, and applications to visual arts. He is involved in several cooperations on artistic application of interactive graphics (theater, art installations, sound and graphic design...). He has collaborated with several artists and designers on the realization of augmented reality environments for art installation, theater plays, or multimedia performances. He has published in major conferences in Computational Linguistics, Information Retrieval, Information Visualization, Multimedia, Digital and Performing arts. Since 2012, he is adviser for arts and culture at University Paris-Sud and has coordinated the arts-science CURIOSITas festival in 2013 & 2014. This festival brings together artists, researchers and students into joint research and creation projects
Kay Chen TAN
Plenary presentation 3: Kay Chen Tan
Title: On Prognostics and Engineering Applications using Evolutionary Multi-objective Optimization
Abstract: Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs. The problem is challenging because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. This talk will provide an overview of evolutionary computation for multi-objective optimization (EMO). It will then present various applications of EMO for solving engineering problems particularly in the area of robust prognostic. As one of the key enablers of condition based maintenance, prognostic involves the core task of determining the remaining useful life (RUL) of the system. This talk will discuss the use of neural network ensembles to improve the prediction accuracy of RUL estimation as well as the use of EMO to optimize the ensemble hyper-parameters. A case study involving the estimation of RUL for turbofan engines will also be presented in the talk.
Kay Chen Tan (SM’05) (SM’08–F’14) received the B.Eng. (Hons.) degree in electronics and electrical engineering and the Ph.D. degree from University of Glasgow, Glasgow, U.K., in 1994 and 1997, respectively. He is an Associate Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, data analytics, prognostics, BCI etc.
Dr Tan has published over 120 journal papers, over 120 papers in conference proceedings, co-authored 5 books. He has been an Invited Keynote/Plenary speaker for over 50 international conferences. He was the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore and is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is currently an elected member of AdCom (2014-2016) and is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society (2011-2013; 2015-2017).
Dr Tan is a Fellow of IEEE. He is also the Editor-in-Chief of IEEE Transactions on Evolutionary Computation. He served as the Editor-in-Chief of IEEE Computational Intelligence Magazine (2010-2013), and currently serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Neural Computing and Applications, Journal of Scheduling, International Journal of Systems Science, etc.
He is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the 2016 IEEE CIS Outstanding TNNLS Paper Award for his paper titled "Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons". He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was felicitated by the International Neural Network Society (INNS) India Regional Chapter (2014) for his outstanding contributions in the field of computational intelligence.
Nikhil R Pal
Plenary presentation 4: Nikhil R Pal
Title: Can I make my neural and neuro-fuzzy systems a bit more useful?
Abstract: Neural networks are easy-to-use wonderful tools for many applications. However, design of any useful “intelligent system” using neural networks raises several important issues. This talk will begin with a brief discussion on these issues with emphasis on three of them. (i) An important system design principle is: "Make everything as simple as possible, but not simpler." (Einstein). This suggests to use a simple architecture. This can be achieved by discarding poor features, reducing the use of dependent (redundant) features, and pruning nodes of the network, as much as possible. (ii) The network should refuse to make any decision when faced with unfamiliar data. In other words, the network should not make a decision when it is given a test data point that is far from the training data that were used to design the network. (iii) The network should be capable of incremental learning. For example, in an industrial application, a new defect may appear and users should be able to augment the network to classify this new defect.
I shall present a general framework on how a neural network or a neuro-fuzzy system can be equipped to select necessary features, discard derogatory features and indifferent features, and control the level of redundancy in the set of selected features. Complete elimination of redundancy is not desirable as then the system may not be able to tolerate any measurement error. This would be an integrated mechanism without requiring evaluation of different subsets of features. Moreover, such a system will be able to exploit any possible nonlinear interaction between features as well as that between features and the tool, which is a neural network here. The same concept will then be extended to prune neural networks as well as to design self-evolving recurrent type-2 neural-fuzzy systems. The model will then be generalized to sensor selection where a sensor is responsible for a set of features. I shall also discuss how a network can be designed so that it can say “don’t know”, when it is appropriate. The incremental learning ability will come as a by-product. Some applications will also be highlighted. Our philosophy is quite general in nature and can be used with other learning tools.
Nikhil R. Pal is an INAE Chair Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes bioinformatics, brain science, fuzzy logic, neural networks, machine learning, and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems (January 2005-December 2010). He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, Fuzzy Sets and Systems, Fuzzy Information and Engineering : An International Journal, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems Man and Cybernetics B (currently IEEE Transactions on Cybernetics).
He is a recipient of the 2015 Fuzzy Systems Pioneer Award. He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He was a member of the Administrative Committee of the IEEE Computational Intelligence Society (CIS) and is a Distinguished Lecturer of the IEEE CIS. At present he is the Vice President for Publications of the IEEE CIS (2013-2016).
He is a Fellow of the National Academy of Sciences, India; the Indian National Academy of Engineering; the Indian National Science Academy, the International Fuzzy Systems Association (IFSA), and IEEE, USA.