Date: January 18, 2021

Time:15:30-17:00 pm

Venue: Room 0411, Teaching Building 0#, Jiuli Campus

Event Details:

Lecturer:Professor Yang, Xin

About the Lecturer:

Professor Yang, Xin, Doctoral Supervisor, Doctor of Engineering. His main research directions are Data Mining and Knowledge Discovery, Three Multi-granular Learning, Financial Intelligence and Business Intelligence. He has published more than 40 academic papers, 2 ESI highly cited papers (first author) papers in the international journals such as INFORM SCIENCES, INFORM FUSION, KNOWL-BASED SYS, INT J APPROX REASON, and in the domestic journals such as Computer Science, Journal of Intelligent Systems, Journal of Nanjing University (Natural Sciences), and also in the international conferences such as IJCRS, ISKE, CIS, etc. He has presided over 4 projects from the National Natural Science Foundation of China, 1 project from the Humanities and Social Sciences Fund of the Ministry of Education, 2 provincial scientific research projects and 1 provincial education reform project. He has participated in the editing of 1 academic monograph and 4 textbooks. He has won the Nomination Award for Outstanding Doctoral Candidates of the ACM Branch, the Outstanding Paper Award of the China Granular Computing and Knowledge Discovery Academic Conference, and the third prize of the First Young Teacher Teaching Competition in Sichuan Province. 

About the Lecture:

Three-branch decision-making based on granular computing is a new method to deal with uncertain information. Through the simple idea of dividing and conquering and reducing complexity to simplicity, it provides people with fast, low-cost, high-yield and fault-tolerant solutions to complex data problems. This report mainly uses cognitive learning and granular computing ideas, with the method of  multi-stage, multi-level and multi-perspective dynamic three-branch decision-making, and with the technology of collaborative incremental update matrix calculation,  to introduce a three-branch cognition oriented to dynamic data Multi-granular learning framework. Then this report discusses three granular computing models and algorithms that deal with multi-dimensional, multi-type and mixed dynamic data separately. Finally, it shares some thoughts on the future development of three cognitive multi-granular learning.