Concept Drift Learning with Alternating Learners

TL;DR

This paper introduces an alternating learners ensemble approach for handling concept drift in data streams, combining long-term stable models with short-term transient models to adapt to changing data distributions effectively.

Contribution

It proposes a novel dual-learner framework with online updating and concept-dependent triggers to improve prediction accuracy under concept drift.

Findings

Effective in tracking abrupt and gradual concept changes

Maintains ensemble accuracy through online updates

Adapts to nonstationary data streams efficiently

Abstract

Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in practical applications. A simple dual-learner ensemble strategy, alternating learners framework, is proposed. A long-memory model learns stable concepts from a long relevant time window, while a short-memory model learns transient concepts from a small recent window. The difference in prediction performance of these two models is monitored and induces an alternating policy to select, update and reset the two models. The method features an online updating mechanism to maintain the ensemble accuracy, and a concept-dependent trigger to focus on relevant data. Through empirical studies the method demonstrates effective tracking and prediction when the steaming data carry abrupt and/or gradual changes.