Rule Acquisition Algorithm for Neighborhood Multi-granularity Rough Sets Based on Maximal Granule
CHEN Jingwen1, MA Fumin1, ZHANG Tengfei2, ZENG Yonggang1
1.College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023 2.School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023
Abstract Granular computing based rule acquisition algorithms remedy the defects of rule acquisition algorithms to some extent. However, most of these algorithms can merely deal with categorical data. To further process the numerical or mixed data from the perspective of multi-granularity and multi-level, the neighborhood multi-granularity rough set model is adopted. Through calculating neighborhood multi-granularity condition granules and decision granules, the redundancy relation of condition granules in the process of rule acquisition is analyzed, and thus the redundant condition granules are further pruned. A rule acquisition algorithm for neighborhood multi-granularity rough set based on maximal granule is developed. The validity and superiority of the proposed algorithm are demonstrated by theoretical analysis and comparable experiments.
Fund:Supported by National Natural Science Foundation of China(No.61403184,61105082), Major Program of Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.17KJA120001), Qing Lan Project of Jiangsu Province(No.QL2016), Project of Priority Academic Program Development of Jiangsu Higher Education Institutions(No.PAPD), Graduate Student Scientific Research Innovation Project of Jiangsu Province(No.KYCX17_1210), "1311 Talent Plan" of Nanjing University of Posts and Telecommunications(No.NY2013)
About author:: 陈静雯,女,1993年生,硕士研究生,主要研究方向为粒计算、智能信息处理.E-mail:386549776@qq.com. 马福民(通讯作者),女,1979年生,博士,副教授,主要研究方向为智能信息处理、智能生产系统等.E-mail:fmmatj@126.com. 张腾飞,男,1980年生,博士,教授,主要研究方向为智能信息处理、大数据分析等.E-mail:tfzhang@126.com. 曾永钢,男,1994年生,硕士研究生,主要研究方向为数据挖掘、智能信息处理.E-mail:284764531@qq.com.
[1] WANG S L, DING G Y, ZHONG M. Big Spatial Data Mining // Proc of the IEEE International Conference on Big Data. Washington, USA: IEEE, 2013: 13-21. [2] PAWLAK Z. Rough Sets. International Journal of Computer & Information Sciences, 1982, 11(5): 341-356. [3] ZHANG X, MEI C L, CHEN D G, et al. Multi-confidence Rule Acquisition Oriented Attribute Reduction of Covering Decision Systems via Combinatorial Optimization. Knowledge-Based Systems, 2013, 50(3): 187-197. [4] FENG L, LI T R, RUAN D, et al. A Vague-Rough Set Approach for Uncertain Knowledge Acquisition. Knowledge-Based Systems, 2011, 24(6): 837-843. [5] 闫明月,侯忠生,高 颖.一种面向布尔时间序列的关联规则挖掘算法.控制与决策, 2012, 27(10): 1447-1451. (YAN M Y, HOU Z S, GAO Y. Algorithm of Mining Association Rules for Binary Times Series. Control and Decision, 2012, 27(10): 1447-1451.) [6] HU Q H, MI J S, CHEN D G. Granular Computing Based Machine Learning in the Era of Big Data. Information Sciences, 2017, 378: 242-243. [7] 张清华,王国胤,刘显全.基于最大粒的规则获取算法.模式识别与人工智能, 2012, 25(3): 388-396. (ZHANG Q H, WANG G W, LIU X Q. Rule Acquisition Algorithm Based on Maximal Granule. Pattern Recognition and Artificial Inte-
lligence, 2012, 25(3): 388-396.) [8] 陈泽华,张 裕,谢 刚.基于粒计算的最简决策规则挖掘算法.控制与决策, 2015, 30(1): 143-148. (CHEN Z H, ZHANG Y, XIE G. Mining Algorithm for Concise Decision Rules Based on Granular Computing. Control and Decision, 2015, 30(1): 143-148.) [9] 张 明.粗糙集理论中的知识获取与约简方法的研究.博士学位论文.南京:南京理工大学, 2012. (ZHANG M. Research on Knowledge Acquisition and Reduction in Rough Set Theory. Ph.D Dissertation. Nanjing, China: Nanjing University of Science & Technology, 2012.) [10] DU Y, HU Q H, ZHU P F, et al. Rule Learning for Classification Based on Neighborhood Covering Reduction. Information Sciences, 2011, 181(24): 5457-5467. [11] PAWLAK Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht, The Netherland: Kluwer Academic Publishers, 1991. [12] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970. [13] LIN G P, QIAN Y H, LI J J. NMGRS: Neighborhood-Based Multigranulation Rough Sets. International Journal of Approximate Reasoning, 2012, 53(7): 1080-1093. [14] CHEN D G, ZHANG W X, YEUNG D, et al. Rough Approximations on a Complete Completely Distributive Lattice with Applications to Generalized Rough Sets. Information Sciences, 2006, 176(13): 1829-1848. [15] 胡清华,赵 辉,于达仁.基于邻域粗糙集的符号与数值属性快速约简算法.模式识别与人工智能, 2008, 21(6): 732-738. (HU Q H, ZHAO H, YU D R. Efficient Symbolic and Numerical Attribute Reduction with Neighborhood Rough Sets. Pattern Recognition and Artificial Intelligence, 2008, 21(6): 732-738.)
2017, Vol. 30 
