Chuan Luo received the Ph.D. degree in Computer Science from Southwest Jiaotong University, Chengdu, China, in 2015. Currently, he is an Associate Professor with the College of Computer Science, Sichuan University, Chengdu, China. He was a Visiting Ph.D. Student with the University of Regina, Regina, SK, Canada, in 2014. In Feb. 2019, he was a Visiting Scholar with the Harvard University, Cambridge, MA, USA. His current research interests include granular computing, cloud computing, and incremental learning. He won the Natural Science Prize (2nd Grade), awarded by the Ministry of Education of China (2021). He is the recipient of two Best Paper Awards at the 12th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS’16), and the 2012 Joint Rough Set Symposium (JRS’12), a Workshop Best Paper Award at the 2019 IEEE Cyber Science and Technology Congress (CyberSciTech’19), and two Best student Paper Awards at the 2015 International Joint Conference on Rough Sets (IJCRS’15) , and the Joint Conference of 13th China Conference on Rough Sets and Soft Computing (CRSSC’13).
He has published more than 60 research papers in international conferences and journals, such as the IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, Information Sciences, Information Fusion, Pattern Recognition, Knowledge-Based Systems, Expert Systems with Applications, Neurocomputing, International Journal of Approximate Reasoning, etc. He serves as an Editor of Human-Centric Intelligent Systems, Member of China Association of Artificial Intelligence (CAAI) Young Committee, Member of Special Committee of CAAI Granular Computing and Knowledge Discovery. He has served as Program Co-Chair of ICAIP 2021-2022、BDML2020-2021, FCST 2016, Session Chair of PAKDD 2022, IEEE DSInS 2021, IEEE CyberSciTech 2019-22021, CRSSC 2014, Web Chair of CCF BigData 2022, etc.
Scalable Rough Feature Selection
Feature selection is an important topic in data mining and machine learning, which aims to select an optimal feature subset for building effective and explainable prediction models. The use of rough set theory for the task of feature selection has proven remarkably popular and has been applied to many real world applications in recent years. Rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features especially for the inexactness problem in approximate numerical classification. Two critical characteristics of volume and velocity associated with big data presents numerous challenges for traditional rough feature selection. This talk will introduce our recent research works targeting scalable rough feature selection from multiple perspectives: Spark rough hypercuboid approach for scalable feature selection, Large-scale meta-heuristic feature selection based on BPSO assisted rough hypercuboid approach, and RHDOFS: a distributed online algorithm towards scalable streaming feature selection.