A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
This addresses the challenge of maintaining accurate learning in streaming data for applications like weather or finance, though it appears incremental as it builds on existing probabilistic and Bayesian approaches.
The paper tackles the problem of concept drift and recurring concepts in data streams, proposing SELeCT, a probabilistic method that continuously evaluates the relevance of past experience, resulting in improved classifier accuracy by adapting to changing conditions.
The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar conditions, is relevant to learning an accurate classifier for current data. Learning from irrelevant experience describing a different concept can degrade performance. A system learning from streaming data must identify which recent experience is irrelevant when conditions change and which past experience is relevant when concepts reoccur, \textit{e.g.,} when weather events or financial patterns repeat. Existing streaming approaches either do not consider experience to change in relevance over time and thus cannot handle concept drift, or only consider the recency of experience and thus cannot handle recurring concepts, or only sparsely evaluate relevance and thus fail when concept drift is missed. To enable learning in changing conditions, we propose SELeCT, a probabilistic method for continuously evaluating the relevance of past experience. SELeCT maintains a distinct internal state for each concept, representing relevant experience with a unique classifier. We propose a Bayesian algorithm for estimating state relevance, combining the likelihood of drawing recent observations from a given state with a transition pattern prior based on the system's current state.