Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (1): 35-48.doi: 10.6040/j.issn.1672-3961.0.2024.200

• Machine Learning & Data Mining • Previous Articles    

Robust fuzzy rough set and attribute reduction based on data weights

LI Lu1, WANG Xin2   

  1. LI Lu1, WANG Xin2(1. School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, Anhui, China;
    2. School of Economics and Management, Anhui Jianzhu University, Hefei 230601, Anhui, China
  • Published:2026-02-03

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Abstract: A robust fuzzy rough set model and attribute reduction algorithm were proposed in this paper. This article considered the local density of data samples and quantifies them, using the quantization results to evaluate the noise level of the samples in the overall dataset. The weight of the samples was measured by the level of noise, and a distance measure between sample sets was defined. The fuzzy similarity between samples was replaced by the fuzzy similarity between sample sets, which improved the robustness of the fuzzy similarity and established a robust fuzzy rough set model. Based on the proposed robust fuzzy rough set, the dependency between attributes and classes was defined to evaluate the significance of attribute subsets, and a robust fuzzy rough set attribute reduction algorithm was designed. The experimental results showed that the designed attribute reduction algorithm had stronger robustness and superiority than existing algorithms.

Key words: fuzzy rough set, robustness, noise data, sample weight, fuzzy dependency degree, attribute reduction

CLC Number: 

  • TP181
[1] 宋苏洋, 叶军, 曾广财, 等. 基于优化可辨识矩阵的多粒度粗糙集属性约简算法[J]. 山东大学学报(理学版), 2024, 59(5): 52-62. SONG Suyang, YE Jun, ZENG Guangcai, et al.Multi-granularity rough set attribute reduction algorithm based on optimized discernibility matrix[J]. Journal of Shandong University(Natural Science), 2024, 59(5): 52-62.
[2] YANG J, QIN X D, WANG G Y, et al. Attribute reduction for hierarchical classification based on improved fuzzy rough set[J]. Information Sciences, 2024, 677: 120900.
[3] CHEN Y P, DING W P, JU H R, et al. A distributed attribute reduction based on neighborhood evidential conflict with Apache Spark[J]. Information Sciences, 2024, 668: 120521.
[4] CUI S G, LI G S, SANG B B, et al. Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction[J]. Applied Soft Computing, 2024, 159: 111656.
[5] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International Journal of General Systems, 1990, 17(2/3): 191-209.
[6] KOU G, YANG P, PENG Y, et al. Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods[J]. Applied Soft Computing, 2020, 86: 105836.
[7] HU Q H, ZHANG L, AN S, et al. On robust fuzzy rough set models[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(4): 636-651.
[8] WANG C Y, WAN L J. New results on granular variable precision fuzzy rough sets based on fuzzy(co)implications[J]. Fuzzy Sets and Systems, 2021, 423: 149-169.
[9] AN S, HU Q H, WANG C Z. Probability granular distance-based fuzzy rough set model[J]. Applied Soft Computing, 2021, 102: 107064.
[10] WANG C Z, HUANG Y, SHAO M W, et al. Fuzzy rough set-based attribute reduction using distance measures[J]. Knowledge-Based Systems, 2019, 164: 205-212.
[11] YANG X L, CHEN H M, LI T R, et al. A noise-aware fuzzy rough set approach for feature selection[J]. Knowledge-Based Systems, 2022, 250: 109092.
[12] ZHANG X H, OU Q Q, WANG J Q. Variable precision fuzzy rough sets based on overlap functions with application to tumor classification[J]. Information Sciences, 2024, 666: 120451.
[13] ZOU D D, XU Y L, LI L Q, et al. Novel variable precision fuzzy rough sets and three-way decision model with three strategies[J]. Information Sciences, 2023, 629: 222-248.
[14] LIANG P, LEI D F, CHIN K S, et al. Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures[J]. Knowledge-Based Systems, 2022, 255: 109795.
[15] SANG B B, CHEN H M, WAN J H, et al. Self-adaptive weighted interaction feature selection based on robust fuzzy dominance rough sets for monotonic classification[J]. Knowledge-Based Systems, 2022, 253(11): 109523.
[16] BAI H X, JING J H, LI D Y, et al. A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data[J]. Applied Soft Computing, 2024, 162: 111848.
[17] THEERENS A, CORNELIS C. On the granular representation of fuzzy quantifier-based fuzzy rough sets[J]. Information Sciences, 2024, 665: 120385.
[18] SHU T X, LIN Y J, GUO L. Online hierarchical streaming feature selection based on adaptive neighborhood rough set[J]. Applied Soft Computing, 2024, 152: 111276.
[19] WANG Z H, CHEN H M, YANG X L, et al. Fuzzy rough dimensionality reduction: a feature set partition-based approach[J]. Information Sciences, 2023, 644: 119266.
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