A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams

TL;DR

This paper introduces SELeCT, a probabilistic framework that dynamically assesses the relevance of past experience in data streams, enabling classifiers to adapt to changing and recurring concepts effectively.

Contribution

It presents a novel Bayesian approach for evaluating the relevance of past experience, allowing continuous adaptation to concept drift and recurrence in streaming data.

Findings

Successfully distinguishes relevant from irrelevant past experience

Handles both concept drift and recurrence effectively

Improves classification accuracy in dynamic data streams

Abstract

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.