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A group incremental feature selection based on knowledge granularity under the context of clustering

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Abstract

As a widely used data preprocessing method, feature selection with rough sets aims to delete redundant conditional features. However, most of the traditional feature selection methods target to the static data set environment, and the importance of features is used as the base for feature selection. These methods only consider the importance of features themselves and do not consider the impact of features on classification. In order to overcome such shortcomings, we first use the information of knowledge granules to calculate the similarity of samples in the same cluster and samples in different clusters; Secondly, from the perspective of clustering, we stick to the principle that the samples in the same cluster are as close as possible, and the samples in different clusters are as far away as possible, then a feature selection model of knowledge granularity (in short SKG) based on the clustering background is designed; Thirdly, in order to make the SKG model adapt to the reduction of dynamic data sets, we discuss the incremental learning mechanism of sample and feature changes, and two incremental models SKGOA and SKGAA are designed to deal with the dynamic feature reduction when some samples and features are added into the decision system. Finally, some numerical experiments are conducted to assess the performance of the proposed algorithms, and the results shown that our approaches are of a prominent advantage in terms of computational time and classification accuracy.

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Data availability

The data that support the findings of this study are openly available in UCIrvine Machine Learning Repository at https://archive.ics.uci.edu/datasets, reference [52].

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Acknowledgements

This work is supported by the Natural Science Foundation of China (61836016), the Key Subject of Chaohu University (kj22zdxk01), the Quality Improvement Project of Chaohu university on Discipline Construction(kj21gczx03), the Provincial Natural Science Research Program of Higher Education Institutions of Anhui province (KJ2021A1030).

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Baohua Liang

  2. College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China

    Baohua Liang

  3. Institute of Computer and Artificial Intelligence, Chaohu University, Hefei, 238000, China

    Baohua Liang, Yong Liu & Jiangyin Lu

  4. School of Foreign Languages, Chaohu University, Hefei, 238000, China

    Houjiang He

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  1. Baohua Liang
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  2. Yong Liu
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  4. Houjiang He
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Correspondence to Baohua Liang.

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Liang, B., Liu, Y., Lu, J. et al. A group incremental feature selection based on knowledge granularity under the context of clustering. Int. J. Mach. Learn. & Cyber. 15, 3647–3670 (2024). https://doi.org/10.1007/s13042-024-02113-7

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  • DOI: https://doi.org/10.1007/s13042-024-02113-7

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