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

Year : 2025 | Volume : 15 | Issue : 03 | Page : 26 32
    By

    Priyadarshini,

  • Shirish S. Sane,

  1. Research Scholar, Department of Computer Engineering MET’s Institute of Engineering Bhujbal Knowledge City (BKC),Nashik, Maharashtra, India
  2. Principal and Professor, Department of Computer Engineering, Gokhale’s Education Society R H Sapat College of Engineering, Nashik, Maharashtra, India

Abstract

As the use of real-time data with high dimensions continues to expand across various domains, selecting important features from the dataset is a key step to improve the predictive accuracy and time taken to build a machine learning model. In datasets where not all features are available at the same time and we are unaware of the total number of features, and features arrive at different time stamps, for example, in real-time patient monitoring in a hospital’s intensive care unit (ICU), feature selection becomes even more challenging. In an ICU, patients are continuously monitored using various medical instruments with sensors for monitoring heart rate, blood pressure, oxygen saturation, and electrocardiograms (ECG), etc.These devices stream data at different intervals, often providing updates asynchronously.

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

[This article belongs to Current Trends in Signal Processing ]

How to cite this article:
Priyadarshini, Shirish S. Sane. A Study on Feature Subset Selection in Feature Streams of Dynamic Data. Current Trends in Signal Processing. 2025; 15(03):26-32.
How to cite this URL:
Priyadarshini, Shirish S. Sane. A Study on Feature Subset Selection in Feature Streams of Dynamic Data. Current Trends in Signal Processing. 2025; 15(03):26-32. Available from: https://journals.stmjournals.com/ctsp/article=2025/view=228526


References

  1.  Villa-Blanco C, Bielza C, Larrañaga P. Feature subset selection for data and feature streams: A review. Artif Intell Rev. 2023;56(Suppl 1):1011-62. doi:10.1007/s10462-023-10546-9.
  2. Wang J, Zhao P, Hoi SCH, Jin R. Online feature selection and its applications. IEEE Trans Knowl Data Eng. 2014;26(3):698–710.
  3. Patil DV, Bichkar RS. Issues in optimization of decision tree learning: A survey. Int J Appl Inf Syst. 2012;3:13-29.
  4. William P, Paithankar DN, Yawalkar PM, Korde SK, Rajendra A, et al. Divination of air quality assessment using ensemble machine learning approach. 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India. 2023. pp. 1-10. doi: 10.1109/ICECONF57129.2023.10083751.
  5. Perkins S, Theiler J. Online feature selection using grafting. Proceedings of the Twentieth International Conference on Machine Learning (ICML’03); 2003; Washington, DC, USA. AAAI Press; 2003. p. 592-9.
  6. Zhou J, Foster D, Stine R, Ungar L. Streaming feature selection using alpha-investing. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. 2005. p. 384-93. doi:10.1145/1081870.1081914.
  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.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 12/06/2025
Accepted 10/07/2025
Published 26/09/2025
Publication Time 106 Days
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