Exploiting Hybrid Kernel-Based Fuzzy Complementary Mutual Information for Selecting Features

Yuan Zhong; Chen Hongmei; Wang Zhihong; Li Tianrui

doi:10.7544/issn1000-1239.202111272

Journal of Computer Research and Development > 2023 > 60(5): 1111-1120. > DOI: 10.7544/issn1000-1239.202111272

Yuan Zhong, Chen Hongmei, Wang Zhihong, Li Tianrui. Exploiting Hybrid Kernel-Based Fuzzy Complementary Mutual Information for Selecting Features[J]. Journal of Computer Research and Development, 2023, 60(5): 1111-1120. DOI: 10.7544/issn1000-1239.202111272

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Exploiting Hybrid Kernel-Based Fuzzy Complementary Mutual Information for Selecting Features

School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756
National Engineering Laboratory of Integrated Transportation Big Data Application Technology (Southwest Jiaotong University), Chengdu 611756

Funds: This work was supported by the National Natural Science Foundation of China (61976182, 62076171, 61976245, 61876157), the Sichuan Key Research and Development Project (2020YFG0035), and Sichuan Science and Technology Achievements Transfer and Transformation Demonstration Project (2022ZHCG0005).

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Author Bio:
Yuan Zhong: born in 1991. PhD. His main research interests include granular computing and knowledge discovery

Chen Hongmei: born in 1971. PhD, professor, PhD supervisor. Member of CCF. Her main research interests include granular computing, rough set, and data mining

Wang Zhihong: born in 1993. PhD candidate. Her main research interests include granular computing, rough set, and data mining

Li Tianrui: born in 1969. PhD, professor, PhD supervisor. Member of CCF. His main research interests include granular computing, rough set, and data mining
Received Date: December 23, 2021
Revised Date: August 07, 2022
Available Online: February 26, 2023

Graphical Abstract

Abstract

Abstract

Fuzzy rough set theory is currently receiving a lot of attention in the fields of data mining and machine learning. The theory provides an effective tool to overcome the discretization problem and can be applied directly to numerical or mixed attribute data. In the fuzzy rough set model, fuzzy relations are defined to measure the similarity between objects and numerical attribute values no longer need to be discretized. The theory has been successfully applied to many fields such as attribute reduction, rule extraction, cluster analysis and outlier detection. Information entropy has been introduced into fuzzy rough set theory for the representation of fuzzy and uncertainty information, resulting in different forms of fuzzy uncertainty measures such as fuzzy information entropy, fuzzy complementary entropy, and fuzzy mutual information. However, most of the proposed fuzzy mutual information on decisions is non-monotonic, which may lead to a non-convergent learning algorithm. To this end, the fuzzy complementary mutual information on decisions is defined based on the hybrid kernel fuzzy complementary entropy, which is shown to vary monotonically with features. Then, the feature selection method is explored by using the hybrid kernel-based fuzzy complementary mutual information and a corresponding algorithm is designed. Experimental results show that the proposed algorithm can select fewer features and maintain or improve the classification accuracy in most cases.
- fuzzy rough set theory,
- hybrid kernel,
- complementary entropy,
- uncertainty measure,
- feature selection

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References (35)

References

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