The 10th International Conference on Fuzzy Systems and Data Mining (FSDM 2024)

Abstract: We all want to have a perfect machine learning model for a perfect prediction. There exists unfortunately no universal ML model that suits all. Different applications would have their own performance requirements and unique characteristic of data. By a popular belief of no-free-lunch-theorem, no algorithm in fact is the best under all situations; likewise an algorithm works well outperforming others for one particular dataset may not generalize well in other cases. In this talk, from my two decades of experiences in data mining (well, not always successful), I will be sharing some tips and possibilities in crafting up a humbly-speaking a near-best ML model without reinventing the wheels, for solving supervised learning problems upon adaptive, multi-view, varying resolutions, real-time, big and perhaps super big data of different kinds. Techniques that fix the ins and outs of a ML model ranging from data pre-processing, smart data sampling to hyperparameter optmization are reviewed and shown to you, via a simple methodology called GROOMS for configuring your best bet for machine learning. Case studies of radiology-oriented cancer detection over medical images, IoT data stream mining for human activity recognition and even Lidar point cloud (spatial resolution 1 meter in radius per cycle gives you 800Mb of data!) for a self-flying UAV are presented and discussed. It is hoped that by using an adaptive approach, a ML model would be positioned at its best shape, when it is being applied in different situations.

Biography: Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 432 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine and various special issues of SCIE-indexed journals. Simon is also an active researcher with leading positions such as Vice-chair of IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and Vice-director of International Consortium for Optimization and Modelling in Science and Industry (iCOMSI).

Research Interests: data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications