2021 International Conference on Electronic Information Engineering and Computer Technology(EIECT 2021)
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Prof. Steven Sheng-Uei Guan

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Prof. Steven Guan

Honorary Professor at University of Liverpool

Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU).

Reasearch Area: machine learning, computational intelligence, big data analytics, mobile commerce, modeling,networking, personalization, security, and pseudorandom number generation.

Steven Guan received his BSc. from Tsinghua University and M.Sc. (1987) & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU).He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK. Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and departmentmanager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the ComputingCenter and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & ComputerEngineering Department at National University of Singapore as an associate professor for 8 years.

Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for 8 years. Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired, delivered keynote speech for 80+ international conferences and served in 180+ international conference committees and 20+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, SelfModifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, Incremental Hyper-sphere Partitioning for Classification, Controllable Cellular Automata for Pseudorandom Number Generation, Self Programmable Cellular Automata, Configurable Cellular Automata, Layered Cellular Automata, Transformation Sequencing of Cellular Automata for Pseudorandom Number Generation, Open Communication with Self-Modifying Protocols, etc.

Keynote Speech Topic

Recursive domain decomposition for classification

Abstract 

Three recursive domain decomposition approaches combined with task decomposition are proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. Incremental partitioning based upon hypercubes, hyperplanes, and hyperspheres are considered for recursive domain decomposition. Hypercubes, hyperplanes, or hyperspheres that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithms(GAs). We solve classification problems through a simple and flexible chromosome encoding scheme. A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is also proposed for the proposed hyperplane approach, where the partitioning rules are encoded by linear equations rather than If-Then rules. These incremental learning algorithms are tested with benchmarks and some artificial datasets. The experimental results show that such recursive domain decomposition approaches outperform in both lower- and higher-dimensional problems compared with the original GA.