Concept Drift Detection and Adaptation

Concept drift is known as an unforeseeable change in underlying over time. The phenomenon of concept drift has been recognized as the root cause of decreased effectiveness in many decision-related applications. A promising solution for coping with persistent environmental change and avoiding system performance degradation is to build a detection and adaptive system. This talk will present a set of methods and algorithms that can effectively and accurately detect understand, and adapt concept drift contents include (1) two novel competence models to indirectly measure variations in data distribution through changes in competence. By detecting changes in competence, differences in data distribution can be accurately detected and quantified, then further described in unstructured data streams; (2) algorithms for determining a drift region to identify when and where a concept drift takes place in a data stream, and a local drift degree measurement that can continuously monitor regional density changes. (3) a fuzzy adaptive regression approach to dynamically recognize, train, and store patterns. The approach assigns the membership degree of the upcoming examples belonging to these patterns to identify which pattern the current examples belong to during the modelling process. The new algorithms and techniques can be applied to data-driven prediction in complex real-world environments.

Prof. Dr. Jie Liu

University of Technology Sydney

CIS Distinguished Lecturer

Sponsored by the Computational Intelligence Society under its Distinguished Lecturers Program.
Time: Aug. 17th, 2022 – 8:30 AM (Rio de Janeiro time).
Location: Online event via Zoom.
Distinguished Professor Jie Lu is a scientist in the field of computational intelligence, primarily known for her work in fuzzy transfer learning, concept drift, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, and Australian Laureate Fellow. Currently, Prof Lu is the Director of the Australian Artificial Intelligence Institute (AAII) and Associate Dean (Research Excellence) at the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). She has published over 500 papers in leading journals and conferences; won ten Australian Research Council (ARC) Discovery Projects, one ARC LP project, and led 15 industry projects; and supervised 50 doctoral students to completion. Prof Lu serves as Editor-In-Chief for Knowledge-Based Systems and International Journal of Computational Intelligence Systems. She has delivered over 30 keynote speeches at international conferences. She is the recipient of the IEEE Transactions on Fuzzy Systems Outstanding Paper Award (2019), the Computer Journal Wilkes Award (2018), Australia’s Most Innovative Engineer Award (2019), and the UTS Chancellor’s Medal for Research Excellence (2019).