Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)

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Ensemble Multiview Feature Partitioning for DNA Sequence Classification

Authors
Prashant Prabhakar1, Aditya Kumar2, *, Shubham Kumar1
1Mahatma Gandhi Central University, Motihari, Bihar, India
2Central University of South Bihar, Gaya, Bihar, India
*Corresponding author. Email: ark1111adk@gmail.com
Corresponding Author
Aditya Kumar
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-628-9_14How to use a DOI?
Keywords
ensemble learning; classification; hybrid model; DNA sequence; multi-view
Abstract

With the ever-increasing speed of DNA sequencing technologies, there has been a huge amount of genomic information, making it imperative to seek effective and reliable ways of classifying these genomic contents. This research proposes an approach known as Ensemble Multi-View Feature Set Partitioning (E-FSP) for DNA sequence classification. This proposed method brings together different techniques of feature set partitioning, which entail Random Split, Attribute Bagging, Attribute Clustering, Ferrers Diagram, and Bell Triangle methods. The method will use a support vector machine (SVM) classifier per view, and classifications will be ensembled at different levels to improve accuracy. Experiments conducted on three DNA datasets (Human, Chimpanzee, and Dog) show that E-FSP performs significantly better than traditional single-view and multi-view classification methods. Also, using Friedman ranking tests, it shows that E-FSP has a higher ranking value than all other methods in terms of accuracy.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
Series
Advances in Engineering Research
Publication Date
31 March 2026
ISBN
978-94-6239-628-9
ISSN
2352-5401
DOI
10.2991/978-94-6239-628-9_14How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Prashant Prabhakar
AU  - Aditya Kumar
AU  - Shubham Kumar
PY  - 2026
DA  - 2026/03/31
TI  - Ensemble Multiview Feature Partitioning for DNA Sequence Classification
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 143
EP  - 152
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_14
DO  - 10.2991/978-94-6239-628-9_14
ID  - Prabhakar2026
ER  -