全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于粒计算与区分能力的属性约简算法*

DOI: 10.16451/j.cnki.issn1003-6059.201504005, PP. 327-334

Keywords: 海量数据,粒计算,属性约简,分层抽样,区分能力

Full-Text   Cite this paper   Add to My Lib

Abstract:

传统的属性约简方法将整个数据集一次性装入内存,很难适应大数据背景下的数据分析.为此文中提出基于粒计算与区分能力的属性约简算法.该算法运用统计学中的分层抽样技术,拆分原始大数据集为多个样本子集(粒),在每个粒上运用属性的区分能力进行属性约简,最后将各粒约简结果进行加权融合,得到原始大数据集的属性约简结果.实验表明该算法对海量数据集进行属性约简的可行性和高效性.

References

[1]  Pawlak Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356
[2]  Slowron A, Rauszer C. The Discernibility Matrices and Functions in Information Systems // Sowiński R, ed. Intelligent Decision Support. Dordrecht, The Netherlands: Springer, 1992: 331-362
[3]  Hu X H, Cercone N. Learning in Relational Databases: A Rough Set Approach. Computational Intelligence, 1995, 11(2): 323-338
[4]  Hu F, Wang G Y. Quick Reduction Algorithm Based on Attribute Order. Chinese Journal of Computers, 2007, 30(8): 1429-1435 (in Chinese)(胡 峰,王国胤.属性序下的快速约简算法.计算机学报, 2007, 30(8): 1429-1435)
[5]  Yao Y Y, Zhao Y. Discernibility Matrix Simplication for Constructing Attribute Reducts. Information Sciences, 2009, 179(5): 867-882
[6]  Xu Z Y, Liu Z P, Yang B R, et al. A Quick Attribute Reduction Algorithm with Complexity of max(O(|C||U|), O(|C|2|U/C|)). Chinese Journal of Computers, 2006, 29(3): 391-399 (in Chinese)(徐章艳,刘作鹏,杨炳儒,等.一个复杂度为max(O(|C||U|), O(|C|2|U/C|)) 的快速属性约简算法.计算机学报, 2006, 29(3): 391-399)
[7]  Ge H, Li L S, Yang C J. An Efficient Attribute Reduction Algorithm Based on Conflict Region. Chinese Journal of Computers, 2012, 35(2): 342-350 (in Chinese)(葛 浩,李龙澍,杨传健.基于冲突域的高效属性约简算法.计算机学报, 2012, 35(2): 342-350)
[8]  Miao D Q, Hu G R. A Heuristic Algorithm for Reduction of Know-ledge. Journal of Computer Research & Development, 1999, 36(6): 681-684 (in Chinese)(苗夺谦,胡桂荣.知识约简的一种启发式算法.计算机研究与发展, 1999, 36(6): 681-684)
[9]  Wang G Y, Yu H, Yang D C. Decision Table Reduction Based on Conditional Information Entropy. Chinese Journal of Computers, 2002, 25(7): 759-766 (in Chinese)(王国胤,于 洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759-766)
[10]  Liang J Y, Qian Y H. Information Granules and Entropy Theory in Information Systems. Science in China: Series F, 2008, 51(9): 1427-1444
[11]  Qian Y H, Liang J Y, Pedrycz W, et al. Positive Approximation: An Accelerator for Attribute Reduction in Rough Set Theory. Artificial Intelligence, 2010, 174(9/10): 597-618
[12]  Lu Z C, Qin Z, Zhang Y Q, et al. A Fast Feature Selection Approach Based on Rough Set Boundary Regions. Pattern Recognition Letters, 2014, 36: 81-88
[13]  Li M, Shang C X, Feng S Z, et al. Quick Attribute Reduction in Inconsistent Decision Tables. Information Sciences, 2014, 254: 155-180
[14]  Qian J, Miao D Q, Zhang Z H, et al. Parallel Attribute Reduction Algorithms Using MapReduce. Information Sciences, 2014, 279: 671-690
[15]  Liang J Y, Wang F, Dang C Y, et al. An Efficient Rough Feature Selection Algorithm with a Multi-granulation View. International Journal of Approximate Reasoning, 2012, 53(6): 912-926
[16]  Liu Q, Sun H, Wang H F. The Present Studying State of Granular Computing and Studying of Granular Computing Based on the Semantics of Rough Logic. Chinese Journal of Computers, 2008, 31(4): 543-555 (in Chinese)(刘 清,孙 辉,王洪发.粒计算研究现状及基于Rough逻辑语义的粒计算研究.计算机学报, 2008, 31(4): 543-555)
[17]  Li J C. Application of Sampling Techniques. Beijing, China: Science Press, 2007 (in Chinese)(李金昌.应用抽样技术.北京:科学出版社, 2007)
[18]  Xu Y, Huai J P, Wang Z Q. Reduction Algorithm Based on Discernibility and Its Applications. Chinese Journal of Computers, 2003, 26(1): 97-103 (in Chinese)(徐 燕,怀进鹏,王兆其.基于区分能力大小的启发式约简算法及其应用.计算机学报, 2003, 26(1): 97-103)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133