Incremental vs batch learning: a comparative analysis of efficiency and energy consumption

Fahad Alkamli; Morched Derbali; Tariq Mohamed Ahmed

Author(s)

Fahad Alkamli Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University,Jeddah,Saudi Arabia

Morched Derbali Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University,Jeddah,Saudi Arabia

Tariq Mohamed Ahmed Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University,Jeddah,Saudi Arabia

Abstract

Given the increasing demand for machine learning algorithms suitable for deployment on devices with limited memory and power resources, there is a clear need for frameworks that prioritize power and memory efficiency. A prominent candidate is the River framework. This study compares incremental and batch learning models, with a focus on efficiency and energy consumption. We evaluate the performance of three representative machine learning algorithms, Multinomial Naive Bayes, Logistic Regression, and Adaptive Random Forest, using the River framework, which enables instance-based learning on data streams. The findings indicate that while incremental learning can reduce memory footprint and training time, it incurs higher energy consumption during the early stages. Conversely, batch learning benefits from aggregated data but faces scalability challenges in resource-constrained environments. This paper explores the tradeoffs between the two approaches and guides their applicability in domains such as cybersecurity and IoT.

Publication Details

Page(s) 1836-1846

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 20, Issue: 19, Year: 2025

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Keywords

river framework, batch learning, Data Streams, real-time processing, Concept Drift, incremental learning, online machine learning

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