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Attribute reduction based on directional semi-neighborhood rough set

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Abstract

Neighborhood rough set has become a mature means of attribute reduction for simplifying modeling and knowledge discovery in continuous and mixed data. It principally generates the neighborhood by condition attribute subset that allows monitoring the local class distribution, thereby evaluating the attribute significance. However, traditional neighborhood formed as a circle with a radius may not inspect some special local distributions correctly, especially near the class boundary. To alleviate such a problem, we present a directional semi-neighborhood formulation approach and redefine the corresponding neighborhood rough set for attribute reduction. Essentially, we attempt to form the neighborhood as a semi-circle directing to the homogeneous samples, which enables to reduce the negative influence of heterogeneous samples. Then, a novel neighborhood rough set using the directional semi-neighborhood is induced. Our feature evaluation criterion, i.e., neighborhood approximation quality established by upper approximation may be more satisfactory in reduct searching. Experiments validated on fifteen public benchmark data sets suggest the superiority of our proposal as it is better at selecting more informative condition attributes for building classification models.

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References

  1. Yao YY (1998) Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci 111(1–4):239–259. https://doi.org/10.1016/S0020-0255(98)10006-3

    Article  MathSciNet  MATH  Google Scholar 

  2. Liu CH, Lin BW, Miao DQ (2024) A novel adaptive neighborhood rough sets based on sparrow search algorithm and feature selection. Inf Sci 679:121099. https://doi.org/10.1016/j.ins.2024.121099

    Article  MATH  Google Scholar 

  3. Zhang XY, Zhao WC (2024) Uncertainty measures and feature selection based on composite entropy for generalized multigranulation fuzzy neighborhood rough set. Fuzzy Sets Syst 486:108971. https://doi.org/10.1016/j.fss.2024.108971

    Article  MathSciNet  MATH  Google Scholar 

  4. Ju HR, Ding WP, Yang XB, Gu PP (2023) Bi-directional adaptive neighborhood rough sets based attribute subset selection. Int J Approximate Reasoning 160:108966. https://doi.org/10.1016/j.ijar.2023.108966

    Article  MathSciNet  MATH  Google Scholar 

  5. Zhang BW, Min F, Davide C (2015) Representative-based classification through covering-based neighborhood rough sets. Appl Intell 43:840–854. https://doi.org/10.1007/s10489-015-0687-5

    Article  MATH  Google Scholar 

  6. Liu FL, Zhang BW, Davide C, Wu WZ, Min F (2018) A comparison study of similarity measures for covering-based neighborhood classifiers. Inf Sci 448–449:1–17. https://doi.org/10.1016/j.ins.2018.03.030

    Article  MathSciNet  MATH  Google Scholar 

  7. Al-shami TM (2021) An improvement of rough sets’ accuracy measure using containment neighborhoods with a medical application. Inf Sci 569:110–124. https://doi.org/10.1016/j.ins.2021.04.016

    Article  MathSciNet  MATH  Google Scholar 

  8. Bai JC, Sun BZ, Chu XL, Wang T, Li HT, Huang QC (2022) Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 114:108127. https://doi.org/10.1016/j.asoc.2021.108127

    Article  MATH  Google Scholar 

  9. Liu D, Li JW (2019) Safety monitoring data classification method based on wireless rough network of neighborhood rough sets. Saf Sci 118:103–108. https://doi.org/10.1016/j.ssci.2019.05.004

    Article  MATH  Google Scholar 

  10. Qi GA, Yang B, Li W (2023) Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making. Inf Sci 621:799–843. https://doi.org/10.1016/j.ins.2022.11.054

    Article  MATH  Google Scholar 

  11. Xing Y, Kochunov P, Erp TGM, Ma TZ, Calhoun VD, Du YH (2023) A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia. IEEE J Biomed Health Inform 27(1):215–226. https://doi.org/10.1109/JBHI.2022.3212479

    Article  Google Scholar 

  12. An S, Guo XY, Wang CZ, Cuo G, Dai JH (2023) A soft neighborhood rough set model and its applications. Inf Sci 624:185–199. https://doi.org/10.1016/j.ins.2022.12.074

    Article  MATH  Google Scholar 

  13. Hu M, Guo YT, Chen DG, Tsang ECC, Zhang QS (2023) Attribute reduction based on neighborhood constrained fuzzy rough sets. Knowl-Based Syst 274:110632. https://doi.org/10.1016/j.knosys.2023.110632

    Article  MATH  Google Scholar 

  14. Liu JH, Lin YJ, Du JX, Zhang HB, Chen ZY, Zhang J (2023) ASFS: A novel streaming feature selection for multi-label data based on neighborhood rough set. Appl Intell 53:1707–1724. https://doi.org/10.1007/s10489-022-03366-x

    Article  MATH  Google Scholar 

  15. Sewwandi MAND, Li YF, Zhang JL (2023) A class-specific feature selection and classification approach using neighborhood rough set and k-nearest neighbor theories. Appl Soft Comput 143:110366. https://doi.org/10.1016/j.asoc.2023.110366

    Article  MATH  Google Scholar 

  16. Yin TY, Chen HM, Yuan Z, Li TR, Liu KY (2023) Noise-resistant multilabel fuzzy neighborhood rough sets for feature subset selection. Inf Sci 621:200–226. https://doi.org/10.1016/j.ins.2022.11.060

    Article  MATH  Google Scholar 

  17. Huang ZH, Li JJ (2024) Covering based multi-granulation rough fuzzy sets with applications to feature selection. Expert Systems with Applications 238(Part C), 121908 https://doi.org/10.1016/j.eswa.2023.121908

  18. Turaga VKH, Chebrolu S (2025) Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce. Expert Systems with Applications 260, 125323 https://doi.org/10.1016/j.eswa.2024.125323

  19. Sang BB, Yang L, Chen HM, Xu WH, Zhang XY. Fuzzy rough feature selection using a robust non-linear vague quantifier for ordinal classification. Expert Systems with Applications 230, 120480 https://doi.org/10.1016/j.eswa.2023.120480

  20. Zhang PF, Wang DX, Yu Z, Zhang YJ, Jiang T, Li TR (2024) A multi-scale information fusion-based multiple correlations for unsupervised attribute selection. Information Fusion 106:102276. https://doi.org/10.1016/j.inffus.2024.102276

    Article  MATH  Google Scholar 

  21. Yang YY, Chen DG, Ji ZY, Zhang X, Dong LJ (2024) A two-way accelerator for feature selection using a monotonic fuzzy conditional entropy. Fuzzy Sets Syst 483:108916. https://doi.org/10.1016/j.fss.2024.108916

    Article  MathSciNet  MATH  Google Scholar 

  22. Yang YY, Chen DG, Zhang X, Ji ZY (2022) Covering rough set-based incremental feature selection for mixed decision system. Soft Comput 26:2651–2669. https://doi.org/10.1007/s00500-021-06687-0

    Article  MATH  Google Scholar 

  23. Guo QH, Yang XB, Li M, Qian YH (2025) Collaborative graph neural networks for augmented graphs: A local-to-global perspective. Pattern Recogn 158:111020. https://doi.org/10.1016/j.patcog.2024.111020

    Article  MATH  Google Scholar 

  24. Li Y, Wu XX, Wang XZ (2023) Incremental reduction methods based on granular ball neighborhood rough sets and attribute grouping. Int J Approximate Reasoning 160:108974. https://doi.org/10.1016/j.ijar.2023.108974

    Article  MathSciNet  MATH  Google Scholar 

  25. Sun L, Si SS, Ding WP, Wang XY, Xu JC (2023) TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Information Fusion 95:91–108. https://doi.org/10.1016/j.inffus.2023.02.016

    Article  Google Scholar 

  26. Thuy NN, Wongthanavasu S (2024) Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets. Int J Approximate Reasoning 173:109256. https://doi.org/10.1016/j.ijar.2024.109256

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhao FF, Pang B, Mi JS (2022) A new approach to generalized neighborhood system-based rough sets via convex structures and convex matroids. Inf Sci 612:1187–1205. https://doi.org/10.1016/j.ins.2022.08.084

    Article  MATH  Google Scholar 

  28. Yang L, Qin KY, Sang BB, Xu WH (2021) Dynamic fuzzy neighborhood rough set approach for interval-valued information systems with fuzzy decision. Appl Soft Comput 111:107679. https://doi.org/10.1016/j.asoc.2021.107679

    Article  MATH  Google Scholar 

  29. Chen Y, Yang XB, Li JH, Wang PX, Qian YH (2022) Fusing attribute reduction accelerators. Inf Sci 587:354–370. https://doi.org/10.1016/j.ins.2021.12.047

    Article  MATH  Google Scholar 

  30. Xia SY, Wu SL, Chen XX, Wang GY, Gao XB, Zhang QH, Giem E, Chen ZZ (2023) GRRS: Accurate and efficient neighborhood rough set for feature selection. IEEE Trans Knowl Data Eng 35(9):9281–9294. https://doi.org/10.1109/TKDE.2022.3222447

    Article  MATH  Google Scholar 

  31. Ju HR, Yin T, Huang JS, Ding WP, Yang XB (2023) Sparse mutual granularity-based feature selection and its application of schizophrenia patients. IEEE Transactions on Emerging Topics in Computational Intelligence, 1–11 https://doi.org/10.1109/TETCI.2023.3314548

  32. Wang N, Zhao EH (2024) A new method for feature selection based on weighted \(k\)-nearest neighborhood rough set. Expert Syst Appl 238:122324. https://doi.org/10.1016/j.eswa.2023.122324

    Article  Google Scholar 

  33. Raza I, Jamal M, Qureshi R, Shahid A, Vistorte A, Samad M, Ashraf I (2024) Adaptive neighborhood rough set model for hybrid data processing: A case study on Parkinson’s disease behavioral analysis. Sci Rep 14:7635. https://doi.org/10.1038/s41598-024-57547-4

    Article  Google Scholar 

  34. Qian DM, Liu KY, Zhang SM, Yang XB (2024) Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy. Appl Intell. https://doi.org/10.1007/s10489-024-05578-9

    Article  MATH  Google Scholar 

  35. Zhang PF, Li TR, Yuan Z, Luo C, Liu KY, Yang XL (2022) Heterogeneous feature selection based on neighborhood combination entropy. IEEE Transactions on Neural Networks and Learning Systems, 1–14 https://doi.org/10.1109/TNNLS.2022.3193929

  36. Hu QH, Zhang L, Zhang D, Pan W, An S, Pedrycz W (2011) Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst Appl 38:10737–10750. https://doi.org/10.1016/j.eswa.2011.01.023

    Article  MATH  Google Scholar 

  37. Yang YY, Song SJ, Chen DG, Zhang X (2020) Discernible neighborhood counting based incremental feature selection for heterogeneous data. Int J Mach Learn Cybern 11:1115–1127. https://doi.org/10.1007/s13042-019-00997-4

    Article  MATH  Google Scholar 

  38. Li JZ, Yang XB, Song XN, Li JH, Wang PX, Yu DJ (2019) Neighborhood attribute reduction: A multi-criterion approach. Int J Mach Learn Cybern 10:731–742. https://doi.org/10.1007/s13042-017-0758-5

    Article  MATH  Google Scholar 

  39. Ding WP, Sun Y, Li M, Liu J, Ju HR, Huang JS, Lin CT (2022) A novel spark-based attribute reduction and neighborhood classification for rough evidence. IEEE Transactions on Cybernetics, 1–14 https://doi.org/10.1109/TCYB.2022.3208130

  40. Xu JC, Shen KL, Sun L (2022) Multi-label feature selection based on fuzzy neighborhood rough sets. Complex & Intelligent Systems 8, 2105–2129 https://doi.org/10.1007/s40747-021-00636-y

  41. Zhang PF, Li TR, Yuan Z, Luo C, Wang GQ, Liu J, Du SD (2022) A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data. Information Fusion 80:87–103. https://doi.org/10.1016/j.inffus.2021.10.017

    Article  MATH  Google Scholar 

  42. Wang CZ, Huang Y, Shao MW, Hu QH, Chen DG (2020) Feature selection based on neighborhood self-information. IEEE Transactions on Cybernetics 50(9):4031–4042. https://doi.org/10.1109/TCYB.2019.2923430

    Article  MATH  Google Scholar 

  43. Mariello A, Battiti R (2018) Feature selection based on the neighborhood entropy. IEEE Transactions on Neural Networks and Learning Systems 29(12):6313–6322. https://doi.org/10.1109/TNNLS.2018.2830700

    Article  MATH  Google Scholar 

  44. Liu KY, Li TR, Yang XB, Ju HR, Yang X, Liu D (2023) Feature selection in threes: Neighborhood relevancy, redundancy, and granularity interactivity. Appl Soft Comput 146:110679. https://doi.org/10.1016/j.asoc.2023.110679

    Article  MATH  Google Scholar 

  45. Qu KL, Gao P, Dai Q, Shun Y, Hua X (2025) Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set. Inf Sci 686:121340. https://doi.org/10.1016/j.ins.2024.121340

    Article  MATH  Google Scholar 

  46. Hu QH, Yu DR, Xie ZX (2008) Neighborhood classifiers. Expert Syst Appl 34:866–876. https://doi.org/10.1016/j.eswa.2006.10.043

    Article  MATH  Google Scholar 

  47. Wu SZ, Wang LT, Ge SY, Hao ZW, Liu YL (2023) Neighborhood rough set with neighborhood equivalence relation for feature selection. Knowl Inf Syst. https://doi.org/10.1007/s10115-023-01999-z

    Article  MATH  Google Scholar 

  48. Ba J, Wang PX, Yang XB, Yu HL, Yu DJ (2023) GLEE: A granularity filter for feature selection. Eng Appl Artif Intell 122:106080. https://doi.org/10.1016/j.engappai.2023.106080

    Article  MATH  Google Scholar 

  49. Wang CZ, Hu QH, Wang XZ, Chen DG, Qian YH, Dong Z (2018) Feature selection based on neighborhood discrimination index. IEEE Transactions on Neural Networks and Learning Systems 29(7):2986–2999. https://doi.org/10.1109/TNNLS.2017.2710422

    Article  MathSciNet  MATH  Google Scholar 

  50. Hu QH, Pedrycz W, Yu DR, Lang J (2010) Selecting discrete and continuous features based on neighborhood decision error minimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, 1 https://doi.org/10.1109/TSMCB.2009.2024166

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Acknowledgements

This work is supported by the National Science Foundation of China (No. 62076111) and the National College Students’ Innovation and Entrepreneurship Training Plan Program (No. 202410289027Z).

Funding

This study was funded by the National Science Foundation of China (No. 62076111) and the National College Students’ Innovation and Entrepreneurship Training Plan Program (No. 202410289027Z).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

    Damo Qian, Keyu Liu & Xibei Yang

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Jie Wang & Shiming Zhang

Authors
  1. Damo Qian
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  2. Keyu Liu
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  3. Jie Wang
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  4. Shiming Zhang
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  5. Xibei Yang
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Contributions

Conceptualization: Damo Qian, Keyu Liu, Shiming Zhang; Methodology: Keyu Liu; Formal analysis and investigation: Damo Qian, Keyu Liu, Shiming Zhang; Writing - original draft preparation: Damo Qian; Writing - review and editing: Keyu Liu, Xibei Yang; Funding acquisition: Xibei Yang; Resources: Keyu Liu, Shiming Zhang; Supervision: Keyu Liu, Xibei Yang.

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Correspondence to Keyu Liu.

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Qian, D., Liu, K., Wang, J. et al. Attribute reduction based on directional semi-neighborhood rough set. Int. J. Mach. Learn. & Cyber. 16, 2523–2535 (2025). https://doi.org/10.1007/s13042-024-02406-x

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