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An enhanced cascade ensemble method for big data analysis

IAES International Journal of Artificial Intelligence · Vol. 14 No. 2 (2025) · pp. 963–974

DOI
10.11591/ijai.v14.i2.pp963-974
ISSN
2252-8938, 2089-4872
Published
2025-04-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their performance. Our method involves using stochastic gradient descent (SGD) classifiers, an improved training data separation algorithm, and integrating principal component analysis (PCA) at each ensemble level. We are confident that these enhancements lead to improved results and accuracy. The proposed approach is designed to enhance both the generalization properties and accuracy of the ensemble (3%), while also reducing its training time. Results from modelling on a real-world biomedical dataset demonstrate significant reductions in training duration, improvements in generalization properties, and enhanced accuracy when compared to other possible implementations of the ensemble.

Keywords

Collaboration network of the authors

8 authors · 24 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Lviv Polytechnic National University
  • Comenius University Bratislava
  • University of Birmingham
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Natalya KustraLviv Polytechnic National University4
Stergios-Aristoteles MitoulisUniversity of Birmingham4

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">An enhanced cascade ensemble method for big data analysis</dc:title>
	<dc:creator>Izonin, Ivan</dc:creator>
	<dc:creator>Muzyka, Roman</dc:creator>
	<dc:creator>Tkachenko, Roman</dc:creator>
	<dc:creator>Gregus, Michal</dc:creator>
	<dc:creator>Korzh, Roman</dc:creator>
	<dc:creator>Yemets, Kyrylo</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Big data analysis; Binary classification task; Cascade ensemble; Imbalanced dataset; Kolmogorov-Gabor polynomial; Machine learning; Wiener polynomial;</dc:subject>
	<dc:description xml:lang="en-US">In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their performance. Our method involves using stochastic gradient descent (SGD) classifiers, an improved training data separation algorithm, and integrating principal component analysis (PCA) at each ensemble level. We are confident that these enhancements lead to improved results and accuracy. The proposed approach is designed to enhance both the generalization properties and accuracy of the ensemble (3%), while also reducing its training time. Results from modelling on a real-world biomedical dataset demonstrate significant reductions in training duration, improvements in generalization properties, and enhanced accuracy when compared to other possible implementations of the ensemble.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/26476</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i2.pp963-974</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 2: April 2025; 963-974</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26476/14424</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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