IRO Journals
Journal of Trends in Computer Science and Smart Technology

Adaptive Group Feature Stream Selection for Dynamic Feature Stream

Open Access
Published: 09 October, 2026
Pages: 929-951
  • Priyadarshini I. , Priyadarshini I.
    Research Scholar, Department of Computer Engineering, MET Institute of Engineering, Affiliated to Savitribai Phule Pune University (SPPU), Nashik, India
    Research Scholar, Department of Computer Engineering, MET Institute of Engineering, Affiliated to Savitribai Phule Pune University (SPPU), Nashik, India
  • Shirish Sane Shirish Sane
    Principal, Department of Computer Engineering, Gokhale Education Society, R. H. Sapat College of Engineering, Savitribai Phule Pune University (SPPU), Nashik, India
    Principal, Department of Computer Engineering, Gokhale Education Society, R. H. Sapat College of Engineering, Savitribai Phule Pune University (SPPU), Nashik, India
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How to Cite

I., Priyadarshini, and Shirish Sane. 2026. “Adaptive Group Feature Stream Selection for Dynamic Feature Stream”. Journal of Trends in Computer Science and Smart Technology 8 (4): 929-51. https://doi.org/10.36548/jtcsst.2026.4.002.
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Keywords

Alpha-Investing
Feature Stream Selection
Dynamic Feature Stream
Adaptive Group Feature stream
Multi Stage Filtering

Abstract

Feature selection is a dimensionality reduction technique that identifies relevant features while discarding irrelevant and redundant ones. Traditional feature selection methods require the entire feature space to be known in advance, making them suitable for static datasets. However, in real-world applications such as healthcare monitoring, sensor networks, and image analysis, new features are generated at different time intervals. In dynamic feature streams, the entire feature space is not known beforehand. To address this challenge, this paper proposes Adaptive Group Feature Stream Selection (AGFS), an online feature selection framework for dynamic feature streams. The proposed framework extends the Alpha-Investing algorithm from processing features individually to processing features that arrive in randomly sized groups. It employs a multi-stage processing approach. First, variance filtering removes constant features. Next, Alpha-Investing-based statistical testing selects relevant feature groups. A group-size capping mechanism is introduced to prevent larger feature groups from being discarded during statistical testing. Within each selected group, features are ranked using feature-class correlation, and redundant features are removed using correlation-based redundancy removal. A data-driven threshold, computed from feature-class correlation observed so far, is introduced to help prevent unnecessary elimination of complementary features. An adaptive wealth mechanism rewards the selection of informative features. Experiments conducted on six benchmark datasets using a K-Nearest Neighbours classifier with 5-fold cross-validation, demonstrate that the proposed framework achieves higher average classification accuracy (87.36%) while maintaining a competitive number of selected features compared to existing feature selection methods. The proposed approach is also validated across multiple classifiers and its consistency across classifiers is reported.

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