A Simple Unlearning Framework for Online Learning Under Concept Drifts

Real-world online learning applications often face data coming from changing target functions or distributions. Such changes, called the concept drift, degrade the performance of traditional online learning algorithms. Thus, many existing works focus on detecting concept drift based on statistical evidence. Other works use sliding window or similar mechanisms to select the data that closely reflect current concept. Nevertheless, few works study how the detection and selection techniques can be combined to improve the learning performance. We propose a novel framework on top of existing online learning algorithms to improve the learning performance under concept drifts. The framework detects the possible concept drift by checking whether forgetting some older data may be helpful, and then conduct forgetting through a step called unlearning. The framework effectively results in a dynamic sliding window that selects some data flexibly for different kinds of concept drifts. We design concrete approaches from the framework based on three popular online learning algorithms. Empirical results show that the framework consistently improves those algorithms on ten synthetic data sets and two real-world data sets.