A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective

TL;DR

This paper provides a comprehensive review of recent advances in machine learning addressing data change issues, unifying domain shift and concept drift under a single framework, and offering a systematic overview of current methods and future directions.

Contribution

It unifies the concepts of domain shift and concept drift into a single data change problem and introduces a three-phase categorization scheme for current solutions.

Findings

Unified view of domain shift and concept drift as data change problems

Systematic overview of state-of-the-art methods in the field

Proposed a three-phase problem categorization scheme

Abstract

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges.