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Original Article
Adaptive Ensemble Learning for Accurate Classification of High-Dimensional Data
Rabins Porwal1
1 Department of Computer Application, School of Engineering & Technology (UIET), Chhatrapati Shahu Ji Maharaj University (CSJMU), India.
Published Online: July-August 2026
Pages: 102-111
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260704014
References
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15. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol. 51, no. 2, pp. 181–207, 2003.
16. G. Brown, J. Wyatt, R. Harris and X. Yao, “Diversity creation methods: a survey and categorisation,” Information Fusion, vol. 6, no. 1, pp. 5–20, 2005.
17. G. Martínez-Muñoz, D. Hernández-Lobato and A. Suárez, “An analysis of ensemble pruning techniques based on ordered aggregation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 245–259, 2009.
18. J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006.
19. G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
20. V. Bolón-Canedo, N. Sánchez-Maroño and A. Alonso-Betanzos, “A review of feature selection methods on synthetic data,” Knowledge and Information Systems, vol. 34, no. 3, pp. 483–519, 2013.
21. Y. Yang, H. Chen and J. Zhang, “Ensemble learning for high-dimensional biomedical data: a systematic review,” Briefings in Bioinformatics, vol. 23, no. 4, pp. 1–18, 2022.
22. M. Fernández-Delgado, E. Cernadas, S. Barro and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014.
23. R. Mehta and K. S. Airo, “N-heterocyclic carbene (NHC)-catalyzed transformations for the synthesis of heterocycles,” Research Journal of Recent Sciences, vol. 13, no. 2, pp. 6–18, 2024.
24. R. Mehta and K. Shani, “Synthesis of sulphur heterocyclic compounds and study of expected biological activities,” Airo International Research Journal, vol. 4, no. 3, pp. 132–146, 2022.
25. R. Mehta and K. Shani, “Synthesis and biological activities of some heterocyclic compounds containing imidazole and beta-lactam moiety,” ZENITH International Journal of Multidisciplinary Research, vol. 11, no. 1, pp. 89–103, 2021.
26. R. Mehta and K. Shani, “An analysis on heterocyclic compounds and their chemical properties,” Airo International Research Journal, vol. 1, no. 1, pp. 44–60, 2023.
2. H. Peng, F. Long and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005.
3. M. Robnik-Šikonja and I. Kononenko, “Theoretical and empirical analysis of ReliefF and RReliefF,” Machine Learning, vol. 53, no. 1–2, pp. 23–69, 2003.
4. H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society: Series B, vol. 67, no. 2, pp. 301–320, 2005.
5. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
6. Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
7. T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832–844, 1998.
8. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
9. J. J. Rodríguez, L. I. Kuncheva and C. J. Alonso, “Rotation Forest: a new classifier ensemble method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619–1630, 2006.
10. T. Chen and C. Guestrin, “XGBoost: a scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
11. G. Ke, Q. Meng, T. Finley, et al., “LightGBM: a highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30, pp. 3146–3154, 2017.
12. A. H. R. Ko, R. Sabourin and A. S. Britto, “From dynamic classifier selection to dynamic ensemble selection,” Pattern Recognition, vol. 41, no. 5, pp. 1718–1731, 2008.
13. R. M. O. Cruz, R. Sabourin and G. D. C. Cavalcanti, “Dynamic classifier selection: recent advances and perspectives,” Information Fusion, vol. 41, pp. 195–216, 2018.
14. D. H. Wolpert, “Stacked generalization,” Neural Networks, vol. 5, no. 2, pp. 241–259, 1992.
15. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol. 51, no. 2, pp. 181–207, 2003.
16. G. Brown, J. Wyatt, R. Harris and X. Yao, “Diversity creation methods: a survey and categorisation,” Information Fusion, vol. 6, no. 1, pp. 5–20, 2005.
17. G. Martínez-Muñoz, D. Hernández-Lobato and A. Suárez, “An analysis of ensemble pruning techniques based on ordered aggregation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 245–259, 2009.
18. J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006.
19. G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
20. V. Bolón-Canedo, N. Sánchez-Maroño and A. Alonso-Betanzos, “A review of feature selection methods on synthetic data,” Knowledge and Information Systems, vol. 34, no. 3, pp. 483–519, 2013.
21. Y. Yang, H. Chen and J. Zhang, “Ensemble learning for high-dimensional biomedical data: a systematic review,” Briefings in Bioinformatics, vol. 23, no. 4, pp. 1–18, 2022.
22. M. Fernández-Delgado, E. Cernadas, S. Barro and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014.
23. R. Mehta and K. S. Airo, “N-heterocyclic carbene (NHC)-catalyzed transformations for the synthesis of heterocycles,” Research Journal of Recent Sciences, vol. 13, no. 2, pp. 6–18, 2024.
24. R. Mehta and K. Shani, “Synthesis of sulphur heterocyclic compounds and study of expected biological activities,” Airo International Research Journal, vol. 4, no. 3, pp. 132–146, 2022.
25. R. Mehta and K. Shani, “Synthesis and biological activities of some heterocyclic compounds containing imidazole and beta-lactam moiety,” ZENITH International Journal of Multidisciplinary Research, vol. 11, no. 1, pp. 89–103, 2021.
26. R. Mehta and K. Shani, “An analysis on heterocyclic compounds and their chemical properties,” Airo International Research Journal, vol. 1, no. 1, pp. 44–60, 2023.
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