Hybridization with Rough Sets
Dr. Sushmita Mitra
Indian Statistical Institute, India
Monday, July 19
10:30h - 11:30h
Rough sets were introduced by Z. Pawlak in 1982 as a formal approximation of a crisp set in terms of a pair of sets, which provide the lower and the upper approximation of the original set. Rough sets provide an important and mathematically established tool for dimensionality reduction in large data. A lot of research has been undertaken, over the last decade, for integrating rough sets into the broader framework of computational intelligence. I will try to provide an overview of such hybridization, along with some applications.
The talk begins with a brief introduction to rough sets. This will be followed by describing some of the hybridizations of rough sets with neural networks, fuzzy sets and genetic algorithms, in well-known tasks of pattern recognition and data mining. Applications are presented for knowledge encoding, rule extraction, dimensionality reduction and clustering. Results demonstrate the suitability of the methodology for feature selection with improved recognition, in diverse domains like microarray gene expressions and face recognition.
Dr. Sushmita Mitra is a Professor at the Machine Intelligence Unit, Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada in 2004, 2007; Meiji University, Japan in 1999, 2004, 2005, 2007; and Aalborg University Esbjerg, Denmark in 2002, 2003. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, and the CIMPA-INRIA-UNESCO Fellowship in 1996.
She is the author of the books "Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing" and "Data Mining: Multimedia, Soft Computing, and Bioinformatics" published by John Wiley, and "Introduction to Machine Learning and Bioinformatics", Chapman & Hall/CRC Press, beside a host of other edited books. Dr. Mitra has guest edited special issues of several journals, is an Associate Editor of "Neurocomputing" and a Founding Associate Editor of "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (WIRE DMKD)". She has more than 75 research publications in referred international journals. According to the Science Citation Index (SCI), two of her papers have been ranked 3rd and 15th in the list of Top-cited papers in Engineering Science from India during 1992--2001. She is listed as one of the top 100 Women Scientists, in Lilavati's Daughters: The Women Scientists of India, published by the Indian Academy of Sciences in 2008.
Dr. Mitra is a Senior Member of IEEE, and Fellow of the Indian National Academy of Engineering and The National Academy of Sciences, India. She served in the capacity of Program Chair, Tutorial Chair, Plenary Speaker, and as member of programme committees of many international conferences. Her current research interests include data mining, pattern recognition, soft computing, image processing, and Bioinformatics.
Temporal Aspects in Data Mining
Prof. Dr. habil. Rudolf Kruse
Otto-von-Guericke-Universität Magdeburg, Germany
Tuesday, July 20
10:30h - 11:30h
In many application areas data is being collected over a long time. Due to its temporal nature such data not only captures influences, like market forces or the launch of a competing product, but also reflects the changes of the underlying domain. Often, change can mean a risk or an opportunity. In either case, it is in many domains not only imperative to detect change in order to survive or to win but also it is inevitable for successful decision making to analyze and act upon it. For example, churning customer groups must be identified as such before they comprise a considerable portion of the target group. Quality issues associated with a vehicle manufacturer have to be treated before affecting thousands of units.
As a response to these requirements there is an increasing research interest in methods that aim at analyzing the changes within a domain. Two different research areas can be distinguished. The area of temporal data mining aims at finding patterns which repeat in time, whereas change mining aims to describe how the results of data mining, models and patterns, evolve over time. While being considerably different in their objectives both areas have in common that they often employ frequent pattern mining approaches, either to express or to analyze changes.
In this talk I will contrast both fields from an application point of view on the example of frequent pattern mining. Within the area of temporal data mining I will discuss how association rules can be modified to allow for constraining temporal relations such as "occurred before" or "happen while". Within the area of change mining I will discuss how frequent patterns can be analyzed for their change characteristics using statistical, template-based and visual approaches. I will give examples of successful industrial applications dealing with different models of incorporating the temporal aspects. More specific, applications with automobile manufacturers and telecommunication providers will be presented and provide evidence of the interest and needs of such analysis methods.
Dr. Rudolf Kruse obtained his diploma (Mathematics) degree in 1979 from University of Braunschweig, Germany, and a PhD in Mathematics in 1980 as well as the venia legendi in Mathematics in 1984 from the same university. Following a short stay at the Fraunhofer Gesellschaft, in 1986 he joined the University of Braunschweig as a professor of computer science. Since 1996 he is a full professor at the Department of Computer Science of the University of Magdeburg where he is leading the computational intelligence research group.
He has carried out research and projects in statistics, artificial intelligence, expert systems, fuzzy control, fuzzy data analysis, computational intelligence, and information mining. His research group is very successful in various industrial applications.
He has coauthored 15 monographs, 15 edited books, as well as more than 330 refereed technical papers in various scientific areas. He is associate editor of several scientific journals. He is a fellow of the International Fuzzy Systems Association (IFSA), fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) and fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Probabilistic Graphical Models and Evolutionary Computation
Prof. Dr. Pedro Larrañaga
Technical University of Madrid, Spain
Wednesday, July 21
10:20h - 11:20h
Three components —representation, inference and learning— are critical in constructing an intelligent system. We need a declarative representation that is a reasonable encoding of our world model. We need to be able to use this representation effectively to answer a broad range of questions that are of interest. And we need to be able to acquire this probability distribution, combining expert knowledge and accumulated data. Probabilistic graphical models support all three capabilities for a broad range of problems.
Evolutionary computation has become an essential tool for solving difficult and high-dimensional optimization problems in a broad range of real problems. Genetic algorithms have been the subject of the major part of such applications. Estimation of distribution algorithms offer a recent evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process.
This talk will review synergies between probabilistic graphical models and evolutionary computation. First, we will show how to use evolutionary computation in inference and in learning from data problems within probabilistic graphical models. The search for the maximum a posteriori assignment and the optimal triangulation of the moral graph will exemplify inference problems. Learning from data may be carried out both in the space of directed acyclic graphs and in the space of orderings. Second, we will illustrate how to use Bayesian networks and Gaussian networks for developing estimation of distribution algorithms in discrete and continuous domains, respectively. Third, recent advances will be presented, covering regularization methods for learning probabilistic graphical models from data, multi-label classification with multidimensional Bayesian networks classifiers and estimation of distribution algorithms based on copulas and Markov networks. The talk will finish with some challenging applications in bioinformatics and neuroscience.
Dr. Pedro Larrañaga received his diploma (Mathematics) degree in 1981 from University of Valladolid, Spain, and a PhD in Computer Science in 1995 from University of the Basque Country, Spain, where he obtained an associate professor level in 1998 and a full professor level in 2004. In 2007 he joined the Technical University of Madrid as full professor at the Department of Artificial Intelligence where he leads the Computational Intelligence group.
His research interests are in the fields of probabilistic graphical models and heuristic optimization. In both fields he has proposed methodological advances and successful applications in industry, computer science and biomedicine.
He has coauthored two edited books on estimation of distribution algorithms, as well as more than 300 scientific papers in different areas. He has participated in more than 70 research projects at national, European and international levels. Since 2007 he is the expert manager of computer technology area of the Spanish Ministry of Science and Innovation.
From Clusters to Models and Perceptions: The Evolution of Fuzzy Clustering
Dr. Enrique H. Ruspini
European Centre for Soft Computing
Mieres, Asturias, Spain
Thursday, July 22
10:20h - 11:20h
Methods that find structures in data, such as cluster analysis techniques, are the object of increased interest, spurred by the availability of large datasets accessible though distributed information networks. Since their inception, clustering approaches based on the theory of fuzzy sets have been extensively applied due to their rich representational capabilities, their formal mathematical underpinnings, and the relations between the nature of fuzzy classifications and utilitarian and metric concepts such as preferences and similarities. Furthermore, the very nature of fuzzy clustering methods readily permits the definition of interesting data structures as instances of paradigmatic models that are approximated by subsets of the dataset being analyzed.
In our presentation, we will review the motivation and evolution of fuzzy-set based methods to discover structures in data. Our point of departure will be the initial proposal for the formulation of relational fuzzy clustering as an optimization problem over the set of all partitions of a subset of a metric space. We will also examine the related problem of partitioning a subset of a vector space and discuss major approaches to its treatment. Continuing our retrospective examination of fuzzy-clustering techniques we will focus on significant milestones in the evolution of this methodology including the generalizations of the notions of prototype, clustering, and fuzzy partition.
The objective of this review is to motivate the introduction of qualitative object description methods, which are soft-computing based approaches for the representation of complex objects in terms of significant qualitative features (e.g., interesting substructures in biological molecules) and by identification of qualitative relationships between those features (e.g., spatial relations between features). The purpose of these methods is the description of complex objects in terms—called perceptions by Zadeh—that are meaningful to domain experts and the study of collections of these objects on the basis of these representations.
The ultimate goal of our presentation, however, is the discussion of recent approaches based on the qualitative description of objects in datasets from multiple viewpoints and the joint study of the results of these classification exercises. These methods, which are well suited for implementation in multiagent, distributed, collaborative environments, extract additional knowledge by fusion and comparative evaluation of the data structures discovered employing different variables, models, and viewpoints.
Dr. Enrique H. Ruspini is a Principal Researcher and Head of the Collaborative Soft Intelligent Systems Laboratory at the European Centre for Soft Computing in Mieres (Asturias), Spain. Dr. Ruspini received his degree of Licenciado en Ciencias Matemáticas from the University of Buenos Aires, Argentina, and his doctoral degree in System Science from the University of California at Los Angeles.
Prior to joining ECSC, he was a Principal Scientist with the Artificial Intelligence Center of SRI International (formerly Stanford Research Institute). Dr. Ruspini has also held positions at the University of Buenos Aires, the University of Southern California, UCLA's Brain Research Institute, and Hewlett-Packard Laboratories.
Dr. Ruspini is one the earliest contributors to the development of fuzzy-set theory and its applications, having introduced its use to the treatment of numerical classification and clustering problems. He has also made significant contributions to the understanding of the foundations of fuzzy logic and approximate-reasoning methods. His recent research has focused on the application of fuzzy-logic techniques to the development of systems for intelligent control of teams of autonomous robots, distributed intelligent control, intelligent data analysis, information retrieval, qualitative description of complex objects, and knowledge discovery and pattern matching in large databases.
Dr. Ruspini, who has lectured extensively in the United States and abroad and is the author of over 100 original research papers, is a Life Fellow of the Institute of Electrical and Electronics Engineers, a First Fellow of the International Fuzzy Systems Association, a Fulbright Scholar, and a SRI Institute Fellow. Dr. Ruspini was the General Chairman of the Second IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'93) and of the 1993 IEEE International Conference on Neural Networks (ICNN'93). In 2004, Dr. Ruspini received the Meritorious Service Award of the IEEE Neural Networks Society for leading the transition of the Neural Networks Council into Society status. He is one of the founding members of the North American Fuzzy Information Processing Society and the recipient of that society's King-Sun Fu Award. Dr. Ruspini is the recipient of the 2009 Fuzzy Systems Pioneer Award of the IEEE Computational Intelligence Society.
Dr. Ruspini is a former member of the IEEE Board of Directors (Division X Director , 2003–2004), the Past-President (President-2001) of the IEEE Neural Networks Council and its past Vice-president of Conferences. Dr. Ruspini, who has led numerous IEEE technical, educational, and organizational activities, is also a member of the Administrative Committee of the IEEE Computational Intelligence Society, and of its Nominations and Appointments and Constitution and Bylaws Committees.
PLATO: Platform for Collaborative Brain System Modeling
Dr. Shiro Usui
RIKEN Brain Science Institute, Japan
Friday, July 23
10:20h - 11:20h
To understand the details of the brain function, a large scale system model that reflects structure and neurophysiological characteristics needs to be implemented. Though numerous computational models of different brain areas have been proposed, its integration for the development of a large scale model have not yet been accomplished because these models were described by different programming languages and mostly because they use different data formats. We introduced a platform for a collaborative brain system modeling (PLATO) where one can construct computational models using several programming languages and connect them at the I/O level with a common data format namely netCDF. As an example, a whole visual system model including eye movement, eye optics, retinal network and visual cortex is being developed. Preliminary results demonstrate that the integrated model successfully simulates the signal processing flow at different stages of the visual system.
Key words: Neuroinformatics, Large scale modeling, Visual system, Common data format
Dr. Shiro Usui received his Ph.D. degree in Electrical Engineering and Computer Science from the University of California, Berkeley in 1974 and became a research assistant at Nagoya University. He moved to Toyohashi University of Technology in 1979 as a lecturer, and has been a professor since 1986. In 2003, he moved to RIKEN Brain Science Institute, Wako Japan as a Head of Neuroinformatics Laboratory and also the Director of Neuroinformatics Japan Center in 2007. His research interests are neuroinformatics, computational neuroscience and physiological engineering in vision science. He is the authors of Neuroinformatics, Mathematical Models of Brain and Neural Systems and several others. He was the President of the Japanese Neural Network Society for 2005 and 2006, and a Fellow of the IEEE and the IEICE.