ReliefF Weighted Neighborhood Rough Sets and Attribute Reduction Based on Random Multi-Attribute Subspaces

doi:10.3778/j.issn.1002-8331.2307-0232

Abstract

Abstract: Attribute reduction is an important preprocessing method for data dimensionality reduction, but most existing attribute reduction methods do not consider the information of attribute weights in information systems. The ReliefF algorithm is a simple and efficient method for evaluating attribute weights. A ReliefF weighted neighborhood rough set and attribute reduction algorithm based on random multi-attribute subspace is proposed in this paper. Firstly, this method generates multiple sets of attribute set partitions with the same size random subspaces. The local weights of attributes in each set of partitioned random subspaces are calculated using the ReliefF algorithm, and the average of the local weights of attributes obtained from all sets is calculated to obtain the final global weights of each attribute in the information system. Then, based on the results of attribute weights, a new weighted neighborhood rough set model is proposed, and the related theories and properties are proved. Finally, based on this model, an attribute reduction algorithm for information systems is proposed by weighting neighborhood dependency. The experimental results of attribute reduction on public datasets show that the proposed algorithm has better reduction performance than the existing algorithms of the same type.

Key words: attribute reduction, ReliefF algorithm, random subspace, weighted neighborhood, neighborhood rough set model

摘要： 属性约简是一种重要的数据降维预处理方法，然而现有的属性约简方法大多没有考虑信息系统中属性权重的信息。ReliefF算法是一种实现简单且运算效率高的属性权重评估方法，提出一种随机多属性子空间的ReliefF加权邻域粗糙集和属性约简算法。该方法生成了多组具有相同大小随机子空间的属性集划分，并对每组划分的随机子空间利用ReliefF算法计算得到属性的局部权重，将所有组得到的属性局部权重求取平均值，得到了信息系统每个属性最终的全局权重；基于属性权重的结果，提出一种新的加权邻域粗糙集模型，并证明了相关理论和性质；在该模型的基础上通过加权邻域依赖度提出一种信息系统的属性约简算法。在公开数据集上的属性约简实验结果表明，所提出的属性约简算法比已有的同类型算法整体上具有更优的约简性能。

关键词: 属性约简, ReliefF算法, 随机子空间, 加权邻域, 邻域粗糙集模型

WANG Li. ReliefF Weighted Neighborhood Rough Sets and Attribute Reduction Based on Random Multi-Attribute Subspaces[J]. Computer Engineering and Applications, 2024, 60(8): 69-77.

王莉. 随机多属性子空间的ReliefF加权邻域粗糙集与属性约简[J]. 计算机工程与应用, 2024, 60(8): 69-77.

References

[1] WANG P, HE J L, LI Z W. Attribute reduction for hybrid data based on fuzzy rough iterative computation model[J]. Information Sciences, 2023, 632: 555-575.
[2] JU H R, DING W P, SHI Z Q, et al. Attribute reduction with personalized information granularity of nearest mutual neighbors[J]. Information Sciences, 2022, 613: 114-138.
[3] ZHANG H Y, SUN Q Q, DONG K Z. Information-theoretic partially labeled heterogeneous feature selection based on neighborhood rough sets[J]. International Journal of Approximate Reasoning, 2023, 154: 200-217.
[4] 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.
[5] HU M, TSANG E C C, GUO Y T, et al. A novel approach to attribute reduction based on weighted neighborhood rough sets[J]. Knowledge-Based Systems, 2021, 220: 106908.
[6] XIE J J, HU B Q, JIANG H B. A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets[J]. International Journal of Approximate Reasoning, 2022, 144: 1-17.
[7] 吴尚智, 任艺璇, 葛舒悦, 等. 用Critic赋权法加权邻域粗糙集的属性约简算法[J]. 北京航空航天大学学报: 1-15[2023-06-17]. https://doi.org/10.13700/j.bh.1001-5965.2022.0975.
WU S Z, REN Y X, GE S Y, et al. An attribute reduction algorithm of weighting neighborhood rough sets with Critic method[J]. Journal of Beijing University of Aeronautics and Astronautics: 1-15[2023-06-17]. https://doi.org/10.13700/j.bh.1001-5965.2022.0975.
[8] 张敏, 朱启兵. 基于加权邻域依赖度的非单调性属性约简[J]. 计算机仿真, 2023, 40（3）: 395-399.
ZHANG M, ZHU Q B. Non-monotonic attribute reduction based on weighted neighborhood dependency[J]. Computer Simulation, 2023, 40（3）: 395-399.
[9] MANTERAS R L. On biases in estimating multi-valued attributes[C]//Proceedings of the International Joint Conference on Artificial Intelligence, 1995: 1034-1040.
[10] GU G Q, LI Z H, HAN J W. Joint feature selection and subspace learning[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 2011: 1294-1547.
[11] DU L, SHENG Y D. Unsupervised feature selection with adaptive structure learning[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015: 209-218.
[12] DONG H, CAI X S, WANG C D. Unsupervised feature selection with multi-subspace randomization and collaboration[J]. Knowledge-Based Systems, 2019, 182: 1-15.
[13] ZHANG B S, LI Y Y, CHAI Z. A novel random multi-subspace based ReliefF for feature selection[J]. Knowledge-Based Systems, 2022, 252: 1-15.
[14] KIRA K, RENDELL L. The feature selection problem: traditional methods and a new algorithm[C]//Proceedings of the 10th National Conference on Artifical Intelligence, 1992: 129-134.
[15] 张腾飞, 张宇迪, 马福民. 基于改进邻域空间的高维混合数据特征选择算法[J]. 控制与决策, 2024, 39（3）：929-938.
ZHANG T F, ZHANG Y D, MA F M. Improved neighborhood space based feature selection algorithm for high-dimensional mixed data[J]. Control and Decision, 2024, 39（3）: 929-938.