A Study on Online Learning Algorithms for Streaming Data

Abstract

The rapid growth of data from sensors, social media, financial systems, and IoT devices has made streaming data processing a critical research area. Traditional batch learning methods are unsuitable for streaming environments due to memory, time constraints, and inability to handle concept drift. Online learning algorithms provide an effective solution by continuously updating models with incoming data. This paper presents a comprehensive study of online learning techniques for streaming data, focusing on adaptability, accuracy, memory efficiency, and performance. Algorithms such as Stochastic Gradient Descent, Online Passive-Aggressive methods, and online ensemble techniques are analyzed along with their mathematical foundations and trade-offs. The study also addresses challenges like non-stationary data, real-time processing, and scalability, along with solutions such as concept drift detection and adaptive learning. Applications in fraud detection, predictive maintenance, healthcare, and recommendation systems are discussed. Experimental results show that hybrid online ensemble methods outperform traditional single-model approaches in stability and performance, while lightweight algorithms are suitable for edge computing. The paper concludes with future directions including federated learning, reinforcement-based adaptation, and integration with deep learning, emphasizing the importance of online learning in real-time intelligent systems.