Optimising Data Processing in Industrial Settings: A Comparative Evaluation of Dimensionality Reduction Approaches

José Cação, José Cação, Mário Antunes, Mário Antunes, José Santos, José Santos, Miguel Monteiro

2024

Abstract

The industrial landscape is undergoing a significant transformation marked by the integration of technology and manufacturing processes, giving rise to the concept of the Industrial Internet of Things (IIoT). IIoT is characterized by the convergence of manufacturing processes, smart IoT devices, and Machine Learning (ML) algorithms, enabling continuous monitoring and optimisation of industrial operations. However, this evolution translates into a substantial increase in the number of interconnected devices and the amount of generated data. Consequently, with ML algorithms facing an exponentially growing volume of data, their performance may decline, and processing times may significantly increase. Dimensionality reduction (DR) techniques emerge as a viable and promising solution, promoting dataset feature reduction and the elimination of irrelevant information. This paper presents a comparative study of various DR techniques applied to a real-world industrial use case, focusing on their impact on the performance and processing times of multiple classification ML techniques. The findings demonstrate the feasibility of applying DR: for a Logistic Regression classifier, minor 4% performance decreases were obtained while achieving remarkable improvements, over 300%, in the processing time of the classifier for multiple DR techniques.

Paper Citation

in Harvard Style

Cação J., Antunes M., Santos J. and Monteiro M. (2024). Optimising Data Processing in Industrial Settings: A Comparative Evaluation of Dimensionality Reduction Approaches. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 119-130. DOI: 10.5220/0012734000003705

in Bibtex Style

@conference{iotbds24,
author={José Cação and Mário Antunes and José Santos and Miguel Monteiro},
title={Optimising Data Processing in Industrial Settings: A Comparative Evaluation of Dimensionality Reduction Approaches},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2024},
pages={119-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012734000003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Optimising Data Processing in Industrial Settings: A Comparative Evaluation of Dimensionality Reduction Approaches
SN - 978-989-758-699-6
AU - Cação J.
AU - Antunes M.
AU - Santos J.
AU - Monteiro M.
PY - 2024
SP - 119
EP - 130
DO - 10.5220/0012734000003705
PB - SciTePress