A Study on Feature Subset Selection in Feature Streams of Dynamic Data

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Open Access

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]03/10/2025 at 11:06 AM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 26 32

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    By

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    Priyadarshini,

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  1. Research Scholar, Department of Computer Engineering MET’s Institute of Engineering Bhujbal Knowledge City (BKC),Nashik, Maharashtra, India

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Abstract

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n7. Wu X, Yu K, Wang H, Ding W. Online streaming feature selection. Proceedings of the 27th International Conference on Machine Learning (ICML’10); 2010; Haifa, Israel. Madison, WI: Omnipress; 2010. p. 1159-66. 8. Hochma Y, Last M. Fast online feature selection in streaming data. Mach Learn. 2025;114:1. doi:10.1007/s10994-024-06712-x. 9. Yu K, Wu X, Ding W, Pei J. Scalable and accurate online feature selection for big data. ACM Trans Knowl Discov Data. 2017;11:1-39. doi:10.1145/2976744. 10. Zhou P, Zhao S, Yan Y, Wu X. Online scalable streaming feature selection via dynamic decision. ACM Trans Knowl Discov Data. 2022;16:1-20. doi:10.1145/3502737. 11. Almusallam N, Tari Z, Chan J, Fahad A, Alabdulatif A, Al-Naeem M. Towards an unsupervised feature selection method for effective dynamic features. IEEE Access. 2021;9:77149-63. doi:10.1109/ACCESS.2021.3082755. 12. Liu H, Setiono R. Chi2: Feature selection and discretization of numeric attributes. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, Herndon, VA, USA. 1995. p. 388-91. doi:10.1109/TAI.1995.479783. 13. Zubaroğlu A, Atalay V. ata stream lusterin : A review. Artif ntell Rev. 2021;54:1201-36. doi:10.1007/s10462-020-09874-x. 14. Zhou P, Hu X, Li P, Wu X. Online streaming feature selection using adapted neighborhood rough set. Inf Sci. 2019;481:258-79. doi:10.1016/j.ins.2018.12.074. 15. Liu J, Lin Y, Du J, Zhang H, Chen Z, Zhang J. ASFS: A novel streaming feature selection for multi- label data based on neighborhood rough set. Appl Intell. 2023;53:1707-24. doi:10.1007/s10489- 022-03366-x. 16. Zhou P, Zhang Y, Yan Y, Zhao S. Unknown type streaming feature selection via maximal information coefficient. 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA. 2022. p. 650-7. doi:10.1109/ICDMW58026.2022.00089. 17. Liu H, Setiono R. Feature selection via discretization. IEEE Trans Knowl Data Eng. 1997 Aug 31;9(4):642-5. 18. Miller A. Subset Selection in Regression. Boca Raton, Fla., USA: Chapman & Hall/CRC; 2002. doi:10.1201/9781420035933. 19. Peng H, Long F, Ding C. Feature selection based on mutual information: Criteria of max- dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27:1226-38. doi:10.1109/TPAMI.2005.159. PubMed PMID: 16119262. 20. Cover TM. Elements of Information Theory. New Jersey, US: John Wiley & Sons; 1999. 21. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B. 1996;58:267- 88. doi:10.1111/j.2517-6161.1996.tb02080.x. 22. Breiman L. Random forests. Mach Learn. 2001;45:5-32. doi:10.1023/A:1010933404324. 23. Sandhiya S, Palani U. A novel hosfs algorithm for online streaming feature selection. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020. p. 1-6. doi:10.1109/ICSCAN49426.2020.9262401. 24. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17); 2017; Long Beach, California, USA. Curran Associates Inc.; 2017. p. 4768-77.nn

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Keywords: Feature stream, feature selection, machine learning, real-time

n[if 424 equals=”Regular Issue”][This article belongs to Current Trends in Signal Processing ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Current Trends in Signal Processing (ctsp)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nPriyadarshini. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]A Study on Feature Subset Selection in Feature Streams of Dynamic Data[/if 2584]. Current Trends in Signal Processing. 26/09/2025; 15(03):26-32.

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nPriyadarshini. [if 2584 equals=”][226 striphtml=1][else]A Study on Feature Subset Selection in Feature Streams of Dynamic Data[/if 2584]. Current Trends in Signal Processing. 26/09/2025; 15(03):26-32. Available from: https://journals.stmjournals.com/ctsp/article=26/09/2025/view=0

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7. Wu X, Yu K, Wang H, Ding W. Online streaming feature selection. Proceedings of the 27th International Conference on Machine Learning (ICML’10); 2010; Haifa, Israel. Madison, WI: Omnipress; 2010. p. 1159-66. 8. Hochma Y, Last M. Fast online feature selection in streaming data. Mach Learn. 2025;114:1. doi:10.1007/s10994-024-06712-x. 9. Yu K, Wu X, Ding W, Pei J. Scalable and accurate online feature selection for big data. ACM Trans Knowl Discov Data. 2017;11:1-39. doi:10.1145/2976744. 10. Zhou P, Zhao S, Yan Y, Wu X. Online scalable streaming feature selection via dynamic decision. ACM Trans Knowl Discov Data. 2022;16:1-20. doi:10.1145/3502737. 11. Almusallam N, Tari Z, Chan J, Fahad A, Alabdulatif A, Al-Naeem M. Towards an unsupervised feature selection method for effective dynamic features. IEEE Access. 2021;9:77149-63. doi:10.1109/ACCESS.2021.3082755. 12. Liu H, Setiono R. Chi2: Feature selection and discretization of numeric attributes. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, Herndon, VA, USA. 1995. p. 388-91. doi:10.1109/TAI.1995.479783. 13. Zubaroğlu A, Atalay V. ata stream lusterin : A review. Artif ntell Rev. 2021;54:1201-36. doi:10.1007/s10462-020-09874-x. 14. Zhou P, Hu X, Li P, Wu X. Online streaming feature selection using adapted neighborhood rough set. Inf Sci. 2019;481:258-79. doi:10.1016/j.ins.2018.12.074. 15. Liu J, Lin Y, Du J, Zhang H, Chen Z, Zhang J. ASFS: A novel streaming feature selection for multi- label data based on neighborhood rough set. Appl Intell. 2023;53:1707-24. doi:10.1007/s10489- 022-03366-x. 16. Zhou P, Zhang Y, Yan Y, Zhao S. Unknown type streaming feature selection via maximal information coefficient. 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA. 2022. p. 650-7. doi:10.1109/ICDMW58026.2022.00089. 17. Liu H, Setiono R. Feature selection via discretization. IEEE Trans Knowl Data Eng. 1997 Aug 31;9(4):642-5. 18. Miller A. Subset Selection in Regression. Boca Raton, Fla., USA: Chapman & Hall/CRC; 2002. doi:10.1201/9781420035933. 19. Peng H, Long F, Ding C. Feature selection based on mutual information: Criteria of max- dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27:1226-38. doi:10.1109/TPAMI.2005.159. PubMed PMID: 16119262. 20. Cover TM. Elements of Information Theory. New Jersey, US: John Wiley & Sons; 1999. 21. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B. 1996;58:267- 88. doi:10.1111/j.2517-6161.1996.tb02080.x. 22. Breiman L. Random forests. Mach Learn. 2001;45:5-32. doi:10.1023/A:1010933404324. 23. Sandhiya S, Palani U. A novel hosfs algorithm for online streaming feature selection. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020. p. 1-6. doi:10.1109/ICSCAN49426.2020.9262401. 24. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17); 2017; Long Beach, California, USA. Curran Associates Inc.; 2017. p. 4768-77.

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Current Trends in Signal Processing

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[if 344 not_equal=””]ISSN: 2277–6176[/if 344]

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 12/06/2025
Accepted 10/07/2025
Published 26/09/2025
Retracted
Publication Time 106 Days

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