A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method

Chengfeng Zheng; Zhizhong Yan; Mohd Shareduwan Mohd Kasihmuddin; Chunqiu Wei; Mohd Asyraf Mansor

doi:10.1145/3625403.3625423

A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method

Chengfeng Zheng^*, Zhizhong Yan^*, Mohd Shareduwan Mohd Kasihmuddin, Chunqiu Wei, Mohd Asyraf Mansor

^*Corresponding author for this work

School of Mathematics and Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This paper proposes a classification methodology that combines neighborhood rough set feature selection with the fuzzy k-nearest neighbor algorithm. The method enhances the accuracy by integrating the fuzzy k-nearest neighbor classifier into the well-established principles of the improved neighborhood rough set feature selection. Through rigorous experimentation, we applied the proposed classification approach to numerically simulate diverse datasets across various application scenarios. The obtained results consistently showcase the high numerical performance achieved by the method. Furthermore, a comprehensive comparative analysis with baseline methods further validates the effectiveness of our approach.

Original language	English
Title of host publication	2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings
Publisher	Association for Computing Machinery
Pages	97-103
Number of pages	7
ISBN (Electronic)	9798400707629
DOIs	https://doi.org/10.1145/3625403.3625423
Publication status	Published - 15 Sept 2023
Event	2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Chengdu, China Duration: 15 Sept 2023 → 17 Sept 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023
Country/Territory	China
City	Chengdu
Period	15/09/23 → 17/09/23

Keywords

Neighborhood rough set
fuzzy k-nearest neighbor
multicategory dataset classification

Access to Document

10.1145/3625403.3625423

Cite this

Zheng, C., Yan, Z., Mohd Kasihmuddin, M. S., Wei, C., & Mansor, M. A. (2023). A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method. In 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings (pp. 97-103). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3625403.3625423

Zheng, Chengfeng ; Yan, Zhizhong ; Mohd Kasihmuddin, Mohd Shareduwan et al. / A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method. 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings. Association for Computing Machinery, 2023. pp. 97-103 (ACM International Conference Proceeding Series).

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title = "A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method",

abstract = "This paper proposes a classification methodology that combines neighborhood rough set feature selection with the fuzzy k-nearest neighbor algorithm. The method enhances the accuracy by integrating the fuzzy k-nearest neighbor classifier into the well-established principles of the improved neighborhood rough set feature selection. Through rigorous experimentation, we applied the proposed classification approach to numerically simulate diverse datasets across various application scenarios. The obtained results consistently showcase the high numerical performance achieved by the method. Furthermore, a comprehensive comparative analysis with baseline methods further validates the effectiveness of our approach.",

keywords = "Neighborhood rough set, fuzzy k-nearest neighbor, multicategory dataset classification",

author = "Chengfeng Zheng and Zhizhong Yan and {Mohd Kasihmuddin}, {Mohd Shareduwan} and Chunqiu Wei and Mansor, {Mohd Asyraf}",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 ; Conference date: 15-09-2023 Through 17-09-2023",

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Zheng, C, Yan, Z, Mohd Kasihmuddin, MS, Wei, C & Mansor, MA 2023, A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method. in 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 97-103, 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023, Chengdu, China, 15/09/23. https://doi.org/10.1145/3625403.3625423

A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method. / Zheng, Chengfeng; Yan, Zhizhong; Mohd Kasihmuddin, Mohd Shareduwan et al.
2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings. Association for Computing Machinery, 2023. p. 97-103 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wei, Chunqiu

AU - Mansor, Mohd Asyraf

PY - 2023/9/15

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N2 - This paper proposes a classification methodology that combines neighborhood rough set feature selection with the fuzzy k-nearest neighbor algorithm. The method enhances the accuracy by integrating the fuzzy k-nearest neighbor classifier into the well-established principles of the improved neighborhood rough set feature selection. Through rigorous experimentation, we applied the proposed classification approach to numerically simulate diverse datasets across various application scenarios. The obtained results consistently showcase the high numerical performance achieved by the method. Furthermore, a comprehensive comparative analysis with baseline methods further validates the effectiveness of our approach.

AB - This paper proposes a classification methodology that combines neighborhood rough set feature selection with the fuzzy k-nearest neighbor algorithm. The method enhances the accuracy by integrating the fuzzy k-nearest neighbor classifier into the well-established principles of the improved neighborhood rough set feature selection. Through rigorous experimentation, we applied the proposed classification approach to numerically simulate diverse datasets across various application scenarios. The obtained results consistently showcase the high numerical performance achieved by the method. Furthermore, a comprehensive comparative analysis with baseline methods further validates the effectiveness of our approach.

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KW - fuzzy k-nearest neighbor

KW - multicategory dataset classification

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Zheng C, Yan Z, Mohd Kasihmuddin MS, Wei C, Mansor MA. A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method. In 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2023 - Conference Proceedings. Association for Computing Machinery. 2023. p. 97-103. (ACM International Conference Proceeding Series). doi: 10.1145/3625403.3625423

A Classification Approach Using Improved Neighborhood Rough Set Feature Selection and Fuzzy K-Nearest Neighbor Method

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