Plenary Speakers

Geometrical Approach to Big Data Václav Snášel VŠB - Technical University of Ostrava, Czech Republic

[Abstract] The Big Data paradigm is one of the main science and technology challenges of today. Big data includes various data sets that are too large or too complex for efficient processing and analysis using traditional as well as unconventional algorithms and tools. The challenge is to derive value from signals buried in an avalanche of noise arising from challenging data volume, flow and validity.
The computer science challenges are as varied as they are important. Whether searching for influential nodes in huge networks, segmenting graphs into meaningful communities, modelling uncertainties in health trends for individual patients, controlling of complex systems, linking data bases with different levels of granularity in space and time, unbiased sampling, connecting with infrastructure involving sensors, and high performance computing, answers to these questions are the key to competitiveness and leadership in this field.
The Big Data is usually modelled as point clouds in a high-dimensional space. One way to understand something about the data is to find a geometric object for which the data looks like a sampling of points. Then the geometric object is seen as an interpolation of the data. Main tool for studying of qualitative features of geometric objects is topology. Topology studies only properties of geometric objects which do not depend on the chosen coordinates, distance, but rather on intrinsic geometric properties of the objects.

[Biography] Václav Snášel is Professor of Computer Science at VŠB - Technical University of Ostrava, Czech Republic. He works s researcher and university teacher. He is Dean Faculty of Electrical Engineering and Computer Science. He is head of research programme IT4 Knowledge management at European center of excellence IT4Innovations.
His research and development experience includes over 30 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, social network, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, nature and Bio-inspired computing, data mining, and applied to various real world problems.
He has given more than 16 plenary lectures and conference tutorials in these areas. He has authored/co-authored several refereed journal/conference papers, books and book chapters.
He has supervised many Ph.D. students from Czech Republic, Slovak Republic, Libya, Jordan, Yemen, China and Vietnam. He supervised 20 PhD students who successfully defended PhD theses.
He is also served as a Guest Editor of number of journals, e.g. Neurocomputing, Elsevier, Journal of Applied Logic, Elsevier etc.
He was responsible investigator and cooperating investigator of 15 research projects in the field of basic and applied research.
He is senior member IEEE, and he is the Chair of IEEE SMC Czechoslovak chapter.

Recent Developments in Evolutionary Computation for Pattern Recognition Mengjie Zhang Victoria University of Wellington, New Zealand

[Abstract] In the talk I will review supervised inductive machine learning algorithms that generate rules and justify why they are a preferred choice for model building in many domains.
First, I will introduce a classical rule learner that is scalable to big data and that is a building block for subsequently developed other algorithms.
Second, I will introduce multiple-instance learning (MIL) problem and algorithm.
In MIL we do not have unique instances corresponding to unique classes but we have "bags" of instances and we know only that a bag is positive if at least one of its instances is positive, or negative if all of its instances are negative.
Third, I will talk about one-class learning where only one, target class, of instances is available and there is no corresponding class information. This type of learning is also known as novelty/outlier detection problem.

[Biography] Mengjie Zhang is currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group with 10 staff members and over 20 PhD students. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) for Faculty of Engineering, and Chair of the Research Committee for the School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of computer vision and image processing, multi-objective optimisation, and feature selection and dimension reduction for classification with high dimensions, classification with unbalanced data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 350 research papers in fully refereed international journals and conferences in these areas. He has been supervising over 100 research thesis and project students including over 30 PhD students.
He has been serving as an associated editor or editorial board member for five international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer), and as a reviewer of over 20 international journals. He has been a major chair for over ten international conferences including IEEE CEC, GECCO, EvoStar and SEAL. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography.
Prof Zhang is a senior member of IEEE and a member of ACM. He is currently chairing the IEEE CIS Evolutionary Computation Technical Committee consisting of over 40 top EC researchers from the five continents and 16 task forces. He is a member of the IEEE CIS Award Committee. He is also a member of IEEE CIS Intelligent System Applications Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.

© 2014 IEEE

© 2015 IEEE