Journal Article

Data stream classification with novel class detection: a review, comparison and challenges

Kumar, Jay, Zhang, Wei, Shao Junming, Din Salah Ud, Bernard, Mawuli Cobbinah, Hasan, Mahmud S M, Yang Qinli

2021

Overview

Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a result, many data stream learning algorithms have been proposed during the past decades and achieve great success in various fields. This paper aims to explore a specific type of challenge in learning evolving data streams, called concept evolution (emergence of novel classes). Concept evolution indicates that the underlying patterns evolve over time, and new patterns (classes) may emerge at any time in streaming data. Therefore, data stream classifiers with emerging class detection have received increasing attention in recent years due to the practical values in many real-world applications. In this article, we provide a comprehensive overview of the existing works in this line of research. We discuss and analyze various aspects of the proposed algorithms for data stream classification with concept evolution detection and adaptation. Additionally, we discuss the potential application areas in which these techniques can be used. We also provide a detailed overview of evaluation measures and datasets used in these studies. Finally, we describe the current research challenges and future directions for data stream classification with novel class detection.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Classification / Classifiers / Data transmission / Evolution / Machine learning