Title: Combining classifier ensembles for efficient big data analytics in edge-cloud environments

Authors: Sirisha Potluri; Khasim Syed; Santi Swarup Basa; J. Kavitha Reddy; P. Pavan Kumar

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India ' School of Computer Science and Engineering, VIT - AP University, Amaravathi, Andhra Pradesh, India ' Department of Computer Science, Maharaja Sriram Chandra Bhanjadeo University, Baripada, Odisha, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India ' Department of Artificial Intelligence and Data Science, Faculty of Science and Technology, The ICFAI Foundation for Higher Education, Hyderabad, Telangana, India

Abstract: Big Data analytics is essential for modern organisations because of the exponential expansion of data from various sources such as social media, sensors, mobile devices and the Internet of Things. By examining this data, organisations can learn a great deal about consumer behaviour, industry trends, operational inefficiencies and creative potential. In our work, we are proposing an efficient ensemble classification model by using metaheuristic optimisation algorithms - Chaotic Pigeon Inspired Optimisation (CPIO), Random Forest (RF) and Support Vector Machine (SVM). Our proposed model performs better feature selection and assigns appropriate class labels for the given cloud service data. This ensemble model supports the classification process and boosts the model's performance. We have performed a series of simulation operations to observe the outcomes under several dimensions and parameters. The resultant outcomes emphasised the efficiency of the proposed model over the recent practices in edge-cloud computing platforms.

Keywords: machine learning; artificial intelligence; cloud computing; edge computing; ensemble classification; big data analytics.

DOI: 10.1504/IJGUC.2025.148548

International Journal of Grid and Utility Computing, 2025 Vol.16 No.5/6, pp.461 - 471

Received: 03 Jun 2024
Accepted: 18 Jul 2024
Published online: 11 Sep 2025 *

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