International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 18, No. 3, 8 Jun. 2026
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Hybrid Machine Learning Approaches for DNA Classification: A Stacking Classifier Perspective
PDF (852KB), PP.104-114
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Author(s)
Sultanul A. Hamim
1,*
Dip Nandi
1
Niloy E. Costa
2
Index Terms
DNA Sequence Classification, Machine Learning, Hybrid Models, Bio-informatics, Predictive Analytics
Abstract
This paper presents a hybrid machine learning model for the classification of DNA sequences by combining different machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, Light Gradient Boosting Machine (LGBM), and XGBoost (XGB). This model has been developed using the stacking ensemble method, associated with a majority voting mechanism to achieve improved overall classification accuracy. In this study, the Promoter Gene Sequences dataset from the UCI Machine Learning Repository was used to concentrate on classifying promoter versus non-promoter sequences. The results indicated an accuracy of 96.25%, showcasing the hybrid model’s ability to classify DNA sequences effectively. This research provides valuable insights into ensemble machine-learning techniques in DNA classification, with possible applications in genomics research, medical diagnostics, agricultural biotechnology, and forensic science. The hybrid model’s thriving implementation demonstrates the potential for more accurate and reliable DNA sequence classification methods.
Cite This Paper
Sultanul A. Hamim, Dip Nandi, Niloy E. Costa, "Hybrid Machine Learning Approaches for DNA Classification: A Stacking Classifier Perspective", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.3, pp.104-114, 2026. DOI:10.5815/ijisa.2026.03.07
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