Chapitre France de la Computational Intelligence Society IEEE
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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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