March 8, 2018

FUZZ-IEEE Tutorials

Room Europa IV Ground Floor Europa II Ground Floor OCEANIA I 2nd Floor OCEANIA II 2nd Floor OCEANIA III 2nd Floor OCEANIA IV 2nd Floor OCEANIA V 2nd Floor OCEANIA VI 2nd Floor OCEANIA VII 2nd Floor OCEANIA VIII 2nd Floor OCEANIA IX 2nd Floor OCEANIA X 2nd Floor
Capacity 300 Poster Session 100 160 100 100 120 150 110 100 180 190
08:00AM-10:00AM HYB_01 – part 1 CEC_01 CEC_06 CEC_10 CEC_15 FUZZ_01 Part 1 FUZZ_05 Part 1 IJCNN_01 Part 1 IJCNN_07 Part 1 IJCNN_12 Part 1
10:15AM-12:15AM HYB_01 – part 2 CEC_02 CEC_07 CEC_11 CEC_16 FUZZ_01 Part 2 FUZZ_05 Part 2 IJCNN_01 Part 2 IJCNN_07 Part 2 IJCNN_12 Part 2
12:15AM-1:00PM LUNCH
1:00PM-3:00PM HYB_02 CEC_03 CEC_08 Part 1 CEC_12 CEC_17 Part 1 FUZZ_02 FUZZ_06 Part 1 IJCNN_03 IJCNN_08 IJCNN_13
3:15PM-5:15PM HYB_03 CEC_04 CEC_08 Part 2 CEC_13 CEC_17 Part 2 FUZZ_03 FUZZ_06 Part 2 IJCNN_04 IJCNN_09 IJCNN_14 Part 1
5:15PM-7:15PM IJCNN_15 CEC_05 CEC_09 CEC_14 CEC_18 FUZZ_04 IJCNN_10 IJCNN_05 IJCNN_06 IJCNN_11 IJCNN_14 Part 2
7:30PM-9:30PM Welcome Reception @ Europa Room – Ground Floor

FUZZ_01 Type-2 Fuzzy Sets and Systems
FUZZ_02 Fuzzy Systems in Medicine and Healthcare
FUZZ_03 Support Fuzzy Machines: From Kernels on Fuzzy Sets to Machine Learning Applications
FUZZ_04 Fuzzy Sets, Computer Science and (Fuzzy) Algorithms
FUZZ_05 Fuzzy Logic and Machine Learning
FUZZ_06 Uncertainty Modeling in Learning from Big Data


Title: Type-2 Fuzzy Sets and Systems (FUZZ_01)

Organized by Christian Wagner, Jon Garibaldi, and Josie McCulloch


General type-2 fuzzy sets and systems are paradigms which enable fine-grained capturing, modelling and reasoning with uncertain information. While recent years have seen increasing numbers of applications from control to intelligent agents and environmental management, the perceived complexity of general type-2 fuzzy sets and systems still makes their adoption a daunting and not time-effective proposition to the majority of researchers.

This tutorial is designed to give researchers a practical introduction to general type-2 fuzzy sets and systems. Over three hours, the modular tutorial will address three main aspects of using and working with general type-2 fuzzy sets and systems:

1. Introduction to General Type-2 Fuzzy Sets and Systems

The first component of the tutorial will provide attendees with a concise and practice-led overview of general type-2 fuzzy sets and systems, reviewing the motivation behind their definition, their structure in relation to type-1 and interval type-2 fuzzy sets and systems, as well as a set of recent applications.

2. Designing General Type-2 Fuzzy Sets and Systems

In the second part of the tutorial, two distinct aspects will be discussed. First, attendees will be given a practical introduction to designing their own general type-2 fuzzy system. Using the online browser-based toolkit JuzzyOnline, participants will be guided in the design of a general type-2 fuzzy system, relating their own design to the design of type-1 fuzzy systems at each stage.

Second, the design of general type-2 fuzzy sets will be discussed through a presentation of a key set of recently introduced processes to create general type-2 fuzzy sets from data.

3. Coding General Type-2 Fuzzy Sets and Systems

The final part of the tutorial will focus on the programmatic implementation and use of general type-2 fuzzy sets and systems. Currently available software tools and toolkit for general type-2 fuzzy sets and system applications will be briefly reviewed, highlighting usage areas from inference to the computation of measures such as similarity and distance. Finally, interested participants will be supported in the development of a simple general type-2 fuzzy system based on the freely available Juzzy, Python and/or R based general type-2 APIs.

Timing: The overall time of the tutorial will be three hours, with an approximately even split over all three tutorial components listed above

Pre-requisites: Basic knowledge of type-1 fuzzy sets and systems is the only pre-requisite for attendees to be able to benefit from this tutorial.

Short Biography

Dr Christian Wagner

Christian Wagner is an Associate Professor in Computer Science at the University of Nottingham, UK and a Visiting Professor in Cybersystems at Michigan Technological University, USA. His research focuses on modelling & handling of uncertain data arising both from qualitative (people) and quantitative sources (e.g. sensors, processes), decision support systems and data-driven policy design; frequently in an inter-disciplinary setting. He has published around 100 peer-reviewed articles, including prize-winning papers in international journals and conferences. He is director of the Lab for Uncertainty in Data and Decision Making (LUCID) with which he and his collaborators recently became runners-up for -both- the best regular and best student papers at the IEEE International Conference on Fuzzy Systems 2016 in Vancouver, Canada. He has attracted over $10 million as principal and co-investigator in the last 6 years. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems journal, is Chair of the IEEE CIS Task Force on Cyber Security and is actively engaged in the academic community, including through the organisation of special sessions and tutorials at premiere IEEE conferences. He has co/developed multiple open source software frameworks, making cutting edge research accessible both to peer researchers as well as to different research communities beyond computer science, including an R toolkit for type-2 fuzzy systems and a new Java based framework for the object oriented implementation of general type-2 fuzzy sets and systems. His current research projects focus on the development, adaptation, deployment and evaluation of artificial intelligence techniques in inter-disciplinary projects bringing together heterogeneous data from stakeholders and quantitative measurements to support informed and transparent decision making in cyber security, environmental management and manufacturing.

Prof Jonathan Garibaldi

Prof. Jon Garibaldi is Head of the Intelligent Modelling and Analysis (IMA) Research Group in the School of Computer Science at the University of Nottingham. His main research interest is in developing intelligent techniques to model human reasoning in uncertain environments, with a particular emphasis on the medical domain. Prof. Garibaldi has been the PI on EU and EPSRC projects worth over £3m, and CoI on a portfolio of grants worth over £25m. He is Director of the University of Nottingham Advanced Data Analysis Centre, established in 2012 to provide leading-edge data analysis services across the University and for industrial consultancy. His experience of leading large research projects includes his roles as Lead Scientist and Co-ordinator of BIOPTRAIN, a Marie-Curie Early Stage Training network in bioinformatics optimisation worth over €2m, the local co -ordinator of the €6.4m BIOPATTERN FP6 Network of Excellence, lead Computer Scientist on a £700k MRC DPFS (Developmental Pathway Funding Scheme) project to transfer the Nottingham Prognostic Index for breast cancer prognosis into clinical use. Industrial projects include a TSB funded project for data analysis in the transport sector, and a collaborative project with CESG (GCHQ) investigating and modelling variation in human reasoning in subjective risk assessments in the context of cyber-security. He is currently the local PI for Nottingham on the £900k UKCRC Joint Funders Tissue Directory and Coordination Centre, a CoI on the £14m BBSRC/EPSRC Synthetic Biology Research Centre in Sustainable Routes to Platform Chemicals, and was CoI on the £10m BBSRC/EPSRC Centre for Plant Integrative Biology. Prof. Garibaldi has published over 200 articles on fuzzy systems and intelligent data analysis, including over 50 journal papers and over 150 conference articles, three book chapters, and three co-edited books. He is an Associate Editor of Soft Computing, was Publications Chair of FUZZ-IEEE 2007 and General Chair of the 2009 UK Workshop on Computational Intelligence, and has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN. He is a member of the IEEE.

Dr Josie McCulloch

Dr McCulloch’s main research focuses on using type-2 fuzzy sets to model uncertain data that has been collected from multiple sources and may contain contradictions. Her work involves the aggregation of such information, and developing useful measures of analysis on the resulting models. She has applied this within the field of Fast Moving Consumer Goods to.


Title: Fuzzy Systems in Medicine and Healthcare (FUZZ_02)

Organized by Uzay Kaymak and J. M. Sousa


Improving access and delivery of health care is a very actual topic in many countries. In order to meet the challenges posed by growing medical costs, aging population and limited resources, new technologies and new methods are being developed. Fuzzy systems play an important role in this context due to their ability to model nonlinear system behavior, to deal with non‐probabilistic uncertainty and to describe model behavior in natural language, making communication with decision makers easier. This tutorial discusses how fuzzy set theory can be used to learn interpretable and transparent models that can be used to support and improve decision making in the healthcare. Cases will be shown regarding (clinical) decision support by using fuzzy set theory, process analysis with fuzzy sets, linguistic summarization of medical data and fuzzy systems‐based support of healthy aging and wellbeing. All of this material will be preceded by a general discussion of the challenges for data‐driven improvement of healthcare processes.

The tutorial will be given in the form of a lecture and consists of two parts. In the first part, a general introduction will be provided regarding the challenges for data‐driven approaches to improving healthcare processes. Afterwards, fuzzy set theory‐based solutions that deal with these challenges will be presented in a broad overview. The overview includes fuzzy modeling techniques, interpretability‐focused fuzzy models, advanced fuzzy models combining fuzziness with probabilistic uncertainty, modeling of linguistic information by using fuzzy sets and flexible information fusion by using fuzzy set theory. In the second part, cases from the industry, in which applications of fuzzy set theory for decision support have played a central role, will be presented. The cases will cover predictive fuzzy modeling for medical decision support, linguistic summarization of medical data and healthcare protocol analysis by using fuzzy sets. The tutorial will complete with an outlook of promising research directions for fuzzy sets in the healthcare domain. The tutorial will take 90 minutes and will be presented by two lecturers in an interactive way.

Intended audience

The tutorial is of interest for researchers, practitioners and graduate‐level students (PhD or advanced Master students) working in the fields of soft computing and computational intelligence, who are interested in fuzzy set applications in medicine and healthcare. The attendants are expected to have a basic knowledge of fuzzy sets and fuzzy systems.


Prof. Uzay Kaymak received the M.Sc. degree in electrical engineering, the Degree of Chartered Designer in information technology, and the Ph.D. degree in control engineering from the Delft University of Technology, Delft, The Netherlands, in 1992, 1995, and 1998, respectively. He has held various positions at Shell International Exploration and Production, Erasmus University Rotterdam, the Netherlands, and Salford University in United Kingdom. He is currently professor of healthcare information systems at the Information Systems (IS) Group of the School of Industrial Engineering of Eindhoven University of Technology. He also chairs the same group. Uzay Kaymak’s research is on intelligent decision support systems, data and process mining and computational modeling methods. His research recent years has concentrated on the development of computational intelligence methods for decision models in which linguistic information, represented either as declarative linguistic rules derived from experts or obtained through natural language processing, is combined with numerical information that is extracted by computational methods. This work has led to the development of semantic text analysis systems for financial decision support, agent‐based behavioral models of human decision making and novel probabilistic fuzzy models for value‐at‐risk estimation. These systems have recently also been shown to be valuable in a clinical setting for predicting mortality rates at an intensive care unit, forming a basis for active patient risk management. Prof. dr. ir. Kaymak was the director of Erasmus Centre of Business Intelligence from 2009 to 2011. Currently, he is leading the healthcare cluster of the IS Group, which research concentrates on the improvement of healthcare processes through the use of process analysis, process re‐engineering, advanced information systems and computerized decision support. He is an internationally acknowledged researcher, who has (co‐)authored more than 250 scientific publications in the fields of intelligent systems, fuzzy decision making, computerized decision support and computational intelligence. He was an associate editor of IEEE Transactions on Fuzzy Systems and serves in the editorial board of several soft computing journals such as Fuzzy Sets and Systems, Soft Computing, and Advances in Fuzzy Systems. He is also a member of the various technical committees of the IEEE and served in the program board of multiple international conferences.

Prof. João Miguel da Costa Sousa, is a Full Professor with the Department of Mechanical Engineering, IST. He received the Ph.D. degree in electrical engineering from the Delft University of Technology, the Netherlands, in 1998. He has authored one book and has authored and co‐authored more than two hundred papers and articles published in journals and conference proceedings. He has supervised more than 30 Ph.D. and M.Sc. students. Prof. Sousa has been an Associate Editor of the IEEE Transactions on Fuzzy Systems, Editor of Mathematics and Computers in Simulation and member of the editorial board from Fuzzy Sets and Systems. He is an active member of the Fuzzy Systems Technical Committee from the IEEE Computational Intelligence Society. He participated in more than 20 research projects, where 5 of them were international projects. He is/was principal investigator in 5 of these projects. He is Research Director of Engineering Systems Fundamentals, in the MIT‐Portugal Program. Prof. Sousa is member and Chair of the Center of Intelligent Systems (CIS), which is a research unit of IDMEC/IST, a private non‐profit association of science, technology and training. CIS contains the area of complex systems, which is focused on new theoretical developments and applications on distributed intelligent optimization of complex systems (modeling and optimization of networked systems) and data analysis in the healthcare by using a combination of soft computing and statistical methods (model selection and validation, bio‐inspired meta‐heuristics and statistics with imperfect and incomplete data).


Title: Support Fuzzy Machines: From Kernels on Fuzzy Sets to Machine Learning Applications (FUZZ_03)

Organized by Jorge Guevara, Roberto Hirata Jr, Stéphane Canu


Most of the time machine learning algorithms consider real-valued attributes as the primal object of interest. However, there are many applications where the data contains set-valued attributes, for example, astronomers usually work with clusters of celestial objects, meteorologists need to perform data science on interval-valued data. Further, imprecise, uncertainty and noise measurements can be modeled by set-valued attributes. Nowadays, the support distribution machines (also called support measure machines) is a widely used technique for solving machine learning tasks on that kind of data. Such approach basically estimates a kernel function between a pair of set-valued attributes, i.e., between the empirical distributions of the points in each set. Thus, the resulting kernel matrix can be used in a kernel machine to solve a particular task. However, there are some common situations where it is more parsimonious to use fuzzy sets for modeling set-valued attributes than empirical distributions, for example, interval-valued datasets, datasets corrupted by noise, datasets with imprecise measurements or datasets with qualitative data. Fuzzy sets can be defined in a simpler way for those examples by using either prior expert knowledge or data-driven approaches.

The aim of this tutorial is to introduce a new class of kernel machines: the support fuzzy machines for solving practical machine learning tasks on datasets containing fuzzy-set valued attributes. Those learning machines learn a functional representation of the fuzzy set-valued data in a Reproducing Kernel Hilbert Space of functions, and in a similar way that support vector machines rely on some support vectors for characterizing the solution, the support fuzzy machines rely on some fuzzy sets. The core idea of this machines is to use positive definite kernels functions on fuzzy sets for defining a gram matrix that can be used for classification, anomaly detection or regression tasks. Such kernels define a similarity measure of fuzzy sets as inner products on Hilbert spaces by using the kernel trick.

Intended audience

Machine learning and data science practitioners, and researchers interested in unconventional data analytics.


Jorge Guevara is a research scientist from IBM Research, his research interest focusses on machine learning and artificial intelligence from theoretical to practical issues. He is interested on devise new ways to extract insights or discovering patters from unconventional data mainly using data from the oil and gas industry. He received is PhD degree from the university of Sao Paulo, Brazil. His thesis was about the design, implementation, and evaluation of classifiers on data given by probability distribution and fuzzy sets. He also worked in the definition of one-class kernel classifiers to detect anomalous distributions, supervised classification of dyslexia and sports data, noisy data classification, hypothesis testing of heterogeneous data, group anomaly detection of astronomical data and feature extraction from speech recognition systems.

Roberto Hirata Jr. is an Associate Professor of the Institute of Mathematics and Statistics (IME) of the University of São Paulo (USP). He received a degree in Physics (Institute of Physics – USP) and one in Mathematics (IME-USP). He received a MSc degree in Computer Science (IME-USP-1997) for his work on morphological segmentation and fast algorithms for basic morphological operators and a PhD degree in Computer Science (IME-USP-2001) for his work on Aperture operators, a class of operators that can be automatically designed using Machine Learning (ML). As part of his PhD’s training, he has worked under Prof. Dr. Edward Russel Dougherty at the Texas A&M University. His work on data analysis and machine learning for bioinformatics was fruitful and he also participated in two cover papers in the prestigious Cancer Research journal. His last works on ML are in the areas of Computer Vision and Fuzzy Logic kernel methods. The main application for the later are anomaly detection and imprecision and uncertainty data analysis. He has advised nine MSc and four PhD projects and he is author or co-author in more than twenty papers in journals or conferences in the last five years.

Stéphane Canu is a Professor of the LITIS research laboratory and of the information technology department, at the National institute of applied science in Rouen (INSA). He received a Ph.D. degree in System Command from Comiègne University of Technology in 1986. He joined the faculty department of Computer Science at Compiegne University of Technology in 1987. He received the French habilitation degree from Paris 6 University. In 1997, he joined the Rouen Applied Sciences National Institute (INSA) as a full professor, where he created the information engineering department. He has been the dean of this department until 2002 when he was named director of the computing service and facilities unit. In 2004 he join for one sabbatical year the machine learning group at ANU/NICTA (Canberra) with Alex Smola and Bob Williamson. In the last five years, he has published approximately thirty papers in refereed conference proceedings or journals in the areas of theory, algorithms and applications using kernel machines learning algorithm and other flexible regression methods. His research interests includes kernels machines, regularization, machine learning applied to signal processing, pattern classification, factorization for recommender systems and learning for context aware applications.


Title: Fuzzy Sets, Computer Science and (Fuzzy) Algorithms (FUZZ_04)

Organized by Rudolf Seising


50 years ago, in 1968, Lotfi A. Zadeh published the paper “Fuzzy Algorithms” in the journal Information and Control. In these years, he was very active in the debate on the education in Computer science that emerged in that time as a new scientific discipline from the field of electrical engineering. As chairman of the Department of Electrical Engineering he was responsible for bringing about the “Berkeley solution”, which ultimately led to the formation of a department for computer science in the College of Letters and Science and a programme in computer science in the College of Engineering within his Department.

In “Computer Science as a Discipline”, which appeared also in 1968, he brought into focus that Computer Science (CS) “cuts across the boundaries of many established fields” and that the parts of CS differ from one another “in degrees of emphasis”. Here he linked his reflection on CS education with fuzzy sets: “Specifically, let us regard computer science as a name for a fuzzy set of subjects and attempt to concretize its meaning by associating with various subjects their respective degrees of containment (ranging from 0 to 1) in the fuzzy set of computer science. For example, a subject such as «programming languages» which plays a central role in computer science will have a degree of containment equal to unity. On the other hand, a peripheral subject such as «mathematical logic» will have a degree of containment of, say, 0.6.”

In a “Containment Table for Computer Science” he arranged the most relevant “subjects in question and their degrees of containment in computer science”. For example, he gave “Theory of Algorithms” the degree of containment of 0.9 in computer science.

Algorithms depend upon precision. An algorithm must be completely unambiguous and error-free in order to result in a solution. The path to a solution amounts to a series of commands, which must be executed in succession. Algorithms formulated mathematically or in a programming language are based on set theory. Each constant and variable is precisely defined, every function and procedure has a definition set and a value set. Each command builds upon them. Successfully running a series of commands requires that each result (output) of the execution of a command lies in the definition range of the following command, that it is, in other words, an element of the input set for the series. Not even the smallest inaccuracies may occur when defining these coordinated definition and value ranges.

However, in “Fuzzy Algorithms” Zadeh fuzzified the commands. Later in an interview, he told me: “I began to see that in real life situations people think certain things. They thought like algorithms but not precisely defined algorithms.” In the paper he wrote. “All people function according to fuzzy algorithms in their daily life […] they use recipes for cooking, consult the instruction manual to fix a TV, follow prescriptions to treat illnesses or heed the appropriate guidance to park a car. Even though activities like this are not normally called algorithms: “For our point of view, however, they may be regarded as very crude forms of fuzzy algorithms”.

Algorithms are very old concepts in mathematics. The may be oldest algorithms that we know is the Euclidean one to compute the greatest common divisor to two numbers. This appeared in his Book VII of his Elements in the 3rd century before Christ.

The “Algorithm” is named after the Persian scholar in the House of Wisdom in Baghdad, Muḥammad ibn Mūsā al-Khwārizmī (82o CE), who wrote a treatise in the Arabic language, which was translated into Latin in the 12th century under the title “Algoritmi de numero Indorum”. This title means “Algoritmi on the numbers of the Indians”, where “Algoritmi” was the translator’s Latinization of Al-Khwarizmi’s name.

Today, algorithms are important in Machine Learning. This history started with the aim to analyze learning behavior with statistical work on biological classification, verbal learning and concept learning as “experiments in induction” in the fields of Statistics and Artificial Intelligence. Algorithms for matching and prediction in data sets established in the 1960s in the new field “Data Analysis”.

Intended audience

The intended audience will be scientists in CI with interests in the historical and philosophical fundamentals of Computer Science, Artificial and Computational Intelligence.

Short Biography

Rudolf Seising received his MS degree (Diplom) in mathematics from the Ruhr-University of Bochum 1986. He received his Ph.D. degree (Dr.) in philosophy of science in 1995 for his work on the philosophy of probability theory in quantum mechanics, and his habilitation degree (PD) of history of science from the LMU of Munich in 2004 for his work on the history of the theory of fuzzy sets. He was a research assistant for computer sciences at the University of the Armed Forces in Munich (1988 – 1995) and for history of sciences at the same university (1995-2002). Since 2002, he has worked as a research assistant for medical expert and knowledge-based systems at the University of Vienna Medical School (since 2004) of the Medical University of Vienna respectively. Rudolf Seising teaches philosophy and history of medicine, medical computer sciences and especially history and philosophy of soft computing. His research interests include the philosophy and history of artificial intelligence.


Title: Fuzzy Logic and Machine Learning (FUZZ_05)Organized by Hamid R. Tizhoosh


In this tutorial, I will talk about the state of the art of fuzzy algorithms in machine learning. In the first part, we will review fuzzy algorithms when they are applied on typical machine-learning tasks such as search, classification, approximation and learning. In the second part, the relationship between fuzzy methods and other machine-learning approaches are reviewed whereas hybrid schemes will be in foreground. In both parts, relevant literature will be reviewed. Matlab/Python examples will be executed/displayed to demonstrate the effect of major methods for relevant applications such as data mining, signal processing, image analysis, and big data. Links to online resources will be included in the material which also contains the source codes and the presentation slides.

The tutorial runs for 3 hours and will cover:

  1. Brief History of Fuzzy Logic
  2. Brief History of Machine Learning
  3. Fuzzy Algorithms for Search, Classification, Approximation and Learning
  4. Fuzzy Algorithms and Other Machine-Learning Methods
  5. Applications: Data Mining, signal processing, image analysis, and big data
  6. Matlab/Python examples At the end of each section/topic, multiple choice questions will be asked via Kahoot online platform such that the audience can participate. This interactive platform with anonymous statistics has received a lot of positive feedback for effective lectures and tutorials.


Dr. Hamid Tizhoosh received the MSc degree in electrical engineering with a major in computer science from University of Technology, Aachen, Germany, in1995. From 1993 to 1996, he worked at Management of Intelligent Technologies Ltd., Aachen, Germany in the field of industrial image processing. Dr. Tizhoosh received his Ph.D. degree from University of Magdeburg, Germany, in2000 with the subject of fuzzy processing of medical images. Dr. Tizhoosh was active as the scientist in the engineering department of IPS, Markham, Canada, until 2001. For six months, he visited the Knowledge/Intelligence Systems Laboratory, University of Toronto, Canada. Since September 2001, Dr. Tizhoosh is a faculty member at the department of Systems Design Engineering, University of Waterloo, Canada. At the same time, he has been the Chief Technology Officer and Chief Executive Officer of Segasist Technologies, a software company (Toronto, Canada) developing innovative software for medical image analysis. His research encompasses machine learning, fuzzy logic and computer vision. Dr. Tizhoosh has extensive experience in medical imaging including portal (megavoltage) imaging, x-rays, MRI and ultrasound. He has been a member of the European Union Projects INFOCUS and ARROW for radiation therapy to improve the integration of online images within the treatment planning of cancer patients. Dr. Tizhoosh has extensively published on fuzzy techniques in image processing. He is the author of two books, 14 book chapters, and more than 100 journal/conference papers.


Title: Uncertainty Modeling in Learning from Big Data (FUZZ_06)

Organized by Xizhao Wang


The tutorial will contain the following content.

  1. Introduction to fuzziness and uncertainty. Uncertainty is a natural phenomenon in machine learning, which can be embedded in the entire process of data preprocessing, learning and reasoning. For example, the training samples are usually imprecise, Incomplete or noisy, the classification boundaries of samples may be fuzzy, and the knowledge used for learning the target concept may be rough. Uncertainty can be used for selecting extended attributes and informative samples in decision tree inductive learning and active learning respectively. If the uncertainty can be effectively modeled and handled during the process of processing and implementation, machine learning algorithms will be more flexible and more efficient. This part will focus on the uncertainty definition and relationships/differences among different uncertainties.
  2. Big data and its 5V features. Big data refers to datasets that are so large that conventional database management and data analysis tools are insufficient to work with them. Big data, which was called massive data, has become a bigger-­‐than-­‐ever problem with the quick developments of data collection and storage technologies. Nowadays, many complex processes can generate big data, for example, there are a greater number of Earth Observing Satellites than ever before, collecting many terabytes of data per day. This part will concentrate on the big data challenges and the current handling strategies.
  3. Modeling uncertainty in big data learning. This is to show that the representation, measure, and handling of uncertainty have a significant impact on the performance of learning algorithms. Usually the modeling/handling of uncertainty is associated with the feature-­‐type and volume of dada. Recent research shows that making clear the change/adaptation of uncertainty with feature-­‐type and volume of data is a very difficult issue. This difficulty is significantly increasing if we deal with the big data. This part will talk mainly about the how to model the uncertainty and how the learning performance is improved through uncertainty handling.
  4. The new challenges that uncertainty brings to big data learning. Big data has two more features, i.e., multimodality and changed-­‐uncertainty. The former means that the types of data can be very complex while the latter indicates that the modeling and measure of uncertainty for big data is significantly different from that for normal sized data. This part will mainly address the critical issue, i.e., big data destroys the fundamental assumption of statistical learning, i.e., the assumption of samples independently identically distributed, and therefore, some new learning theory and methodology need to be re-­‐built.
  5. Concluding remarks. This part will give an overview on learning with uncertainty from big data, summarizing the results acquired in recent years’ study on big data leaning problems. Some remarks on the concept of fuzzy-­‐learning which considers the learning as two categories according to the problem nature are finally presented.

Short Biography

Prof. Wang Xizhao, Ph.D. (1998), Professor (1998), IEEE Fellow (2012), CAAI Fellow (2017), Editor-in-Chief of Springer Journal Machine Learning and Cybernetics (2010), Deputy chair of CAAI machine learning committee (2012), and knowledge engineering committee (2013), overseas high-level (peacock B class) talent of Shenzhen city (2015), prizewinner of First-Class Award of Natural Science Advances of Hebei Province (2007), and Model Teacher of China (2009). University at Canberra. Since March 2014 to now Prof. Wang has moved to college of computer science and software engineering in Shenzhen University as a professor and a director of Big Data Institute.

Prof. Wang’s main research interest is machine learning and uncertainty information processing including inductive learning with fuzzy representation, approximate reasoning and expert systems, neural networks and their sensitivity analysis, statistical learning theory, fuzzy measures and fuzzy integrals, random weight network, and the recent topic: machine learning theories and methodologies in Big-Data environment. The main research feature is, through discovering and representing the uncertainty hidden in big data, to dig the distribution of big data and then use distributed parallel technology to design and implement classification and clustering algorithms which are suitable for different types of big data. It focuses on the corresponding key issues of theory and technology of big data analytics.

Academic contributions: (1) Putting forward the concept of “fuzzy learning from examples” for the first time in 1996 during his PhD thesis, and extending machine learning approaches into the uncertainty framework. His research in this aspect lasted almost 20 years and acquired a series of achievements with significant impact, for example, the project “fuzzy-valued attribute feature subset selection” won the first prize of Hebei province natural science in 2007. (2) Establishing a refinement methodology and technique for similarity based clustering, called departure-0,5, and extending it to a new branch of semi-supervised learning based on the departure-0.5, and further applying successfully to the big data learning. Mainly due to this contribution Prof. Wang was elected as an IEEE Fellow in 2012. (3). Proposing the viewpoint that uncertainty modeling and its effective handling play a crucial/indispensable role in improving the generalization ability for a big data learning system. The view point is highly recognized by the experts in related domains, and is funded by a NSFC key project (Uncertainty modeling in learning from big data, 2018-2022).

Research achievements: Prof. Wang has published 3 monographs and 2 textbooks. He has also published 200+ research papers in different magazine and conferences in the field of machine learning and uncertainty, among which 150+ publications have been included in SCI or EI databases. The journals include IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Machine Learning, Information Sciences and Fuzzy Sets and Systems. By Google scholar in November 2017, the total number of citations is 6360, the maximum number of citations for a single paper is 600, and the SCI-H index is 42. Prof. Wang has completed 30+ research projects including ones funded by National Natural Sciences Fund of China, by Ministry of Education, by National Development and Reform Commission, by Hebei Province Natural Science, and by RGC in Hong Kong.

Awards and honors: Prof. Wang has received the First-Class Award of Natural Science Advances of Hebei Province and the Second-Class Award of Natural Science of Education Ministry in 2007. He was selected as one member of the first hundred of excellent innovative talents of Hebei province in 2007. He gained the honor of Model Teacher of China in 2009. Prof. Wang was evaluated as an IEEE Fellow in 2012 and a CAAI Fellow in 2017. He was chosen as the local leading talent of Shenzhen in 2013 and one of the Chinese scholars whose academic papers have been highly cited based on Elsevier statistics in 2014/15/16. Prof. Wang was identified as the overseas high-level (peacock B class) talent of Shenzhen in 2015.