Chapitre France de la Computational Intelligence Society IEEE
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Séminaire du Professeur Ronald R. Yager
Distinguished lecturer de l’IEEE Computational Intelligence Society
Multi-Criteria Decision Making and Uncertainty
L’IEEE France Section et l’IEEE Computational Intelligence Society ont le plaisir de vous inviter à la présentation du professeur Ronald R. Yager, directeur du Machine Intelligence Institute, Iona College, USA, reçu en tant que distinguished lecturer. La présentation portera sur la décision multicritère dans l'incertain, son résumé est disponible ci-dessous.
Date et horaire : 4 mai 2017 à 10H00
Lieu : LIP6, salle 105 (1er étage), couloir 25-26
4 place Jussieu, 75005 Paris.
Abstract: Multi-Criteria aggregation is a pervasive problem appearing in many technological domains. During this presentation, we shall discuss some issues related to this task. One issue is the modeling of multi-criteria decision functions and a related issue is the evaluation of these decision functions in the face of uncertain information. One case we shall consider is the evaluation of the OWA operator when the satisfaction to the individual criteria is expressed via a probability distribution. We shall also consider the case of interval criteria satisfactions. We shall look at the role of fuzzy measures in the modeling process. One issue that must be dealt with is the ordering of the complex uncertain criteria satisfactions that is required to use the Choquet integral in the criteria aggregation.
Bio: Ronald R. Yager is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College. He is editor-in-chief of the International Journal of Intelligent Systems. He has published over 500 papers and edited over 30 books in areas related to fuzzy sets, human behavioral modeling, decision-making under uncertainty and the fusion of information. He is among the world’s most highly cited researchers with over 57,000 citations in Google Scholar. He was the 2016 recipient of the IEEE Frank Rosenblatt Award, the most prestigious honor given out by the IEEE Computational Intelligence Society. He was the recipient of the IEEE Computational Intelligence Society Pioneer award in Fuzzy Systems. He received the special honorary medal of the 50-th Anniversary of the Polish Academy of Sciences. He received the Lifetime Outstanding Achievement Award from the International Fuzzy Systems Association. He received honorary doctorate degrees, honoris causa, from the Azerbaijan Technical University and the State University of Information Technologies, Sofia Bulgaria. Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences and the Fuzzy Systems Association. He has served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California, Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He was a visiting distinguished scientist at King Saud University, Riyadh Saudi Arabia. He was an honorary professor at Aalborg University in Denmark. He received his undergraduate degree from the City College of New York and his Ph. D. from the Polytechnic Institute New York University. He recently edited a volume entitled Intelligent Methods for Cyber Warfare.
Je suis heureuse de vous annoncer que nous recevrons le président de la IEEE Computational Intelligence Society le jeudi 22 octobre à 10h au LIP6, pour le séminaire suivant:
Ensemble Approaches in Learning
by Xin Yao, Professor at the University of Birmingham (UK), President of the IEEE Computational Intelligence Society
Lieu : salle 105, couloir 25-26, 4 place Jussieu, 75005 Paris
Designing a monolithic system for a large and complex learning task is hard. Divide-and-conquer is a common strategy in tackling such large and complex problems. Ensembles can be regarded an automatic approach towards automatic divide-and-conquer. Many ensemble methods, including boosting, bagging,
negative correlation, etc., have been used in machine learning and data mining for many years. This talk will describe three examples of ensemble methods,
i.e., multi-objective learning, online learning with concept drift, and multi-class imbalance learning. Given the important role of diversity in ensemble methods, some discussions and analyses will be given to gain a better understanding of how and when diversity may help ensemble learning.
Some materials used in the talk are based on the following papers:
A Chandra and X. Yao, ``Ensemble learning using multi-objective evolutionary
algorithms,'' Journal of Mathematical Modelling and Algorithms, 5(4):417-445,
L. L. Minku and X. Yao, "DDD: A New Ensemble Approach For Dealing With Concept
Drift,'' IEEE Transactions on Knowledge and Data Engineering, 24(4):619-633,
S. Wang and X. Yao, ``Multi-Class Imbalance Problems: Analysis and Potential
Solutions,'' IEEE Transactions on Systems, Man and Cybernetics, Part B,
42(4):1119-1130, August 2012.
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and
Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS). His
work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 and 2015 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best
paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the 2013 IEEE CIS Evolutionary Computation Pioneer Award.
His major research interests include evolutionary computation, ensemble learning, and their applications, especially in software engineering.
Plus d'information sur Xin Yao : http://www.cs.bham.ac.uk/~xin/
Présidente, IEEE France Section Computational Intelligence Chapter
Nous sommes heureux d'annoncer le séminaire de Prof. Jim Keller, de l'University of Missouri, aux Étas-Unis, le 28 mai 2015, qui nous parlera de "Does it all add up? A study of fuzzy protoform linguistic summarization of time series", invité par la Computational Intelligence Society, dans le cadre de son Distinguished Lecturer Program. Le résumé de sa présentation est :
Producing linguistic summaries of large databases or temporal sequences of measurements is an endeavor that is receiving increased attention. These summaries can be used in a continuous monitoring situation, like eldercare, where it is important to ascertain if the current summaries represent an abnormal condition. Primarily a human, such as a care giver in the eldercare
example, is the recipient of the set of summaries describing a time range, for example, last night’s activities. However, as the number of sensors and monitored conditions grow, sorting through a fairly large number of summaries can be a burden for the person, i.e., the summaries stop being information and become yet one more pile of data. It is therefore necessary to automatically process sets of summaries to condense the data into more manageable chunks.
The first step towards automatically comparing sets of digests is to determine similarity. For fuzzy protoform based summaries, we developed a natural similarity and proved that the associated dissimilarity is a metric over the space of protoforms. Utilizing that distance measure, we defined and examined several fuzzy set methods to compute dissimilarity between sets of summaries, and most recently utilized these measures to define prototypical behavior over a large number of normal time periods.
In this talk, I will cover the definition of fuzzy protoforms, define our (dis)similarity, outline the proof that it is a metric, discuss the fuzzy aggregation methods for sets of summaries, and show how prototypes are formed and can used to detect abnormal nights. The talk will be loaded with actual examples from our eldercare research. There is much work to be done and hopefully, more questions than answers will result from the discussion.
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