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Uncertainty Measure-Based Incremental Feature Selection For Hierarchical Classification

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Abstract

In the era of big data, there exist complex structure between different classes labels. Hierarchical structure, among others, has become a representative one, which is mathematically depicted as a tree-like structure or directed acyclic graph. Most studies in the literature focus on static feature selection in hierarchical information system. In this study, in order to solve the incremental feature selection problem of hierarchical classification in a dynamic environment, we develop two incremental algorithms for this purpose (IHFSGR-1 and IHFSGR-2 for short). As a preliminary step, we propose a new uncertainty measure to quantify the amount of information contained in the hierarchical classification system, and based on this, we develop a non-incremental hierarchical feature selection algorithm. Next, we investigate the updating mechanism of this uncertainty measure upon the arrival of samples, and propose two strategies for adding and deleting features, leading to the development of two incremental algorithms. Finally, we conduct some comparative experiments with several non-incremental algorithms. The experimental results suggest that compared with several non-incremental algorithms, our incremental algorithms can achieve better performance in terms of the classification accuracy and two hierarchical evaluation metrics, and can significantly accelerate the fuzzy rough set-based hierarchical feature selection.

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Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grants 61976244 and 12001422, the Nature Science Foundation of Shaanxi Province under Grants 2021JQ-580 and 2023-JC-YB-597, and the Postgraduate Innovation and Practice Ability Development Fund of Xi’an Shiyou University under Grant YCS22112071.

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Authors and Affiliations

  1. College of Computer Science, Xi’an Shiyou University, Xi’an, 710065, China

    Yang Tian

  2. College of Science, Xi’an Shiyou University, Xi’an, 710065, China

    Yanhong She

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  1. Yang Tian
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Yang Tian: Conceptualization, Methodology, Software, Validation, Investigation, Formal analysis, Visualization, Writing-original draft; Yanhong She: Conceptualization, Methodology, Writing-original draft, Supervision.

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Correspondence to Yanhong She.

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Tian, Y., She, Y. Uncertainty Measure-Based Incremental Feature Selection For Hierarchical Classification. Int. J. Fuzzy Syst. 26, 2074–2096 (2024). https://doi.org/10.1007/s40815-024-01708-0

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