Handling drifts and shifts in on-line data streams with evolving fuzzy systems

In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their structure (rule base and parameters) is not fixed and not pre-determined, but is extracted from data streams on-line and in an incremental manner. When dealing with so-called drifts and shifts in data streams, one needs to take into account 1) automatic detection of drifts and shifts, and 2) automatic reaction to the drifts and shifts. This is important to avoid interruptions in the learning process and downtrends in predictive accuracy. To address the first problem, we propose an approach based on the concept fuzzy rule age. The second problem is

• Type: Journal
• Year: 2011
• Where: ASC
• Authors: Edwin Lughofer, Plamen P. Angelov
• Keywords: Artificial Intelligence, ASC 2011, Concept Fuzzy Rule, Data Streams, On-line Data Streams