All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

ViewsDownloads

Relative Articles

More...

基于组合加速机制的多特定类快速正域约简
A Fast Attribute Reduction Algorithm Based on Fusing Acceleration Mechanism for Multi-Specific Classes Positive Region

DOI: 10.12677/HJDM.2023.133020, PP. 203-212

Keywords: 粗糙集,属性约简,粒计算,加速机制,Rough Set, Attribute Reduction, Granular Computing, Acceleration Mechanism

Full-Text   Cite this paper   Add to My Lib

Abstract:

信息技术的快速发展给社会带来了海量的高维数据,这些海量数据中隐藏着大量有价值的信息。如何高效处理大规模数据并从中提取有效知识已成为计算机领域的研究热点。基于粗糙集理论的属性约简,可以在保证数据分类能力不变的前提下,删除冗余属性,从而实现数据的有效降维。在实际应用中,决策者往往只关注某些特定决策标签的有效信息提取。在多特定类属性约简中,传统的启发式算法约简效率较低。针对该问题,本文从对象、属性和粒度的视角出发,提出了基于组合加速机制的多特定类快速正域约简算法。最后,实验选取6组数据集进行实验,从约简长度、参与迭代的对象规模、迭代次数和约简时间四个方面验证了所提算法在多特定类约简中的有效性。
The rapid development of information technology has brought massive high-dimensional data to society, which hides a large amount of valuable information. How to efficiently deal with these large-scale data and extract effective knowledge from it has become a research hotspot in the field of computer science. Attribute reduction based on rough set theory can remove redundant attributes while keeping the ability of data classification unchanging, thus reducing the dimension of data effectively. In practical applications, decision makers often only focus on the effective information extraction of certain specific decision labels. In the attribute reduction of multi-specific classes, traditional heuristic algorithms have lower reduction efficiency. To solve above problems, this paper proposes a fast attribute reduction algorithm based on fusing acceleration mechanism for multi-specific classes positive region, which is from the perspectives of objects, attributes and granularity. Finally, six datasets were used for experiments. And the experimental results show the effectiveness of the proposed accelerating algorithm in this paper for multi-specific decision classes attribute reduction, which is verified from four aspects: reduction length, size of objects in iterations, number of iterations and reduction time.

References

[1]  Pawlak, Z. (1982) Rough Sets. International Journal of Computer & Information Sciences, 11, 341-356.
https://doi.org/10.1007/BF01001956
[2]  Shu, W.H., Qian, W.B. and Xie, Y.H. (2020) Incremental Feature Selec-tion for Dynamic Hybrid Data Using Neighborhood Rough Set. Knowledge-Based Systems, 194, Article ID: 105516.
https://doi.org/10.1016/j.knosys.2020.105516
[3]  王国胤, 于洪. 多粒度认知计算——一种大数据智能计算的新模型[J]. 数据与计算发展前沿, 2020, 1(2): 75-85.
https://doi.org/10.11871/jfdc.issn.2096-742X.2019.02.007
[4]  王国胤, 代劲, 李昊. 基于多粒度认知计算的生产安全管理与决策[J]. 中国科学基金, 2021, 35(5): 752-758.
https://doi.org/10.16262/j.cnki.1000-8217.2021.05.012
[5]  Banerjee, A. and Maji, P. (2019) Segmentation of Bi-as Field Induced Brain MR Images Using Rough Sets and Stomped-t Distribution. Information Sciences, 504, 520-545.
https://doi.org/10.1016/j.ins.2019.07.027
[6]  陈超凡, 张红云, 蔡克参, 苗夺谦. 基于三支决策的二阶段图像分类方法[J]. 模式识别与人工智能, 2021, 34(8): 768-776.
https://doi.org/10.16451/j.cnki.issn1003-6059.202108010
[7]  钱文彬, 彭莉莎, 王映龙, 段德林. 不完备混合决策系统的三支决策模型与规则获取方法[J]. 计算机应用研究, 2020, 37(5): 1421-1427.
[8]  Yao, Y.Y. and Zhang, X.Y. (2017) Class-Specific Attribute Reducts in Rough Set Theory. Information Sciences, 418-419, 601-618.
https://doi.org/10.1016/j.ins.2017.08.038
[9]  Li, B.Z., Wei, Z.H., Miao, D.Q., Zhnag, N. and Shen, W. (2020) Improved General Attribute Reduction Algorithms. Information Sciences, 536, 298-316.
https://doi.org/10.1016/j.ins.2020.05.043
[10]  Qian, Y.H., Liang, J.Y., Pedrycz, W. and Dang, C.Y. (2010) Posi-tive Approximation: An Accelerator for Attribute Reduction in Rough Set Theory. Artificial Intelligence, 174, 597-618.
https://doi.org/10.1016/j.artint.2010.04.018
[11]  陈曼如, 张楠, 童向荣, 东野升龙, 杨文静. 基于多尺度属性粒策略的快速正域约简算法[J]. 计算机应用, 2019, 39(12): 3426-3433.
[12]  赵立威, 张楠, 张中喜. 基于特征粒的序决策系统快速约简研究[J]. 山西大学学报, 2020, 43(4): 897-905.
[13]  Chen, Y., Yang, X.B., Li, J.H., Wang, P.X. and Qian, Y.H. (2022) Fusing Attribute Reduction Accelerators. Information Sciences, 587, 354-370.
https://doi.org/10.1016/j.ins.2021.12.047
[14]  Liu, G.L., Hua, Z. and Zou, J.Y. (2018) Local Attribute Reductions for Decision Tables. Information Sciences, 422, 204-217.
https://doi.org/10.1016/j.ins.2017.09.007
[15]  Zhang, X.Y., Yao, H., Lv, Z.Y. and Miao, D.Q. (2021) Class-Specific Information Measures and Attribute Reducts for Hierar-chy and Systematicness. Information Sciences, 563, 196-225.
https://doi.org/10.1016/j.ins.2021.01.080
[16]  Wang, Y.B., Chen, X.J. and Dong, K. (2019) Attribute Reduction via Local Conditional Entropy. International Journal of Ma-chine Learning and Cybernetics, 10, 3619-3634.
https://doi.org/10.1007/s13042-019-00948-z

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413