[1]高学义,张楠,童向荣,等.广义分布保持属性约简研究[J].智能系统学报,2017,12(03):377-385.[doi:10.11992/tis.201704025]
 GAO Xueyi,ZHANG Nan,TONG Xiangrong,et al.Research on attribute reduction using generalized distribution preservation[J].CAAI Transactions on Intelligent Systems,2017,12(03):377-385.[doi:10.11992/tis.201704025]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年03期
页码:
377-385
栏目:
出版日期:
2017-06-25

文章信息/Info

Title:
Research on attribute reduction using generalized distribution preservation
作者:
高学义12 张楠12 童向荣12 姜丽丽12
1. 烟台大学 数据科学与智能技术山东省高校重点实验室, 山东 烟台 264005;
2. 烟台大学 计算机与控制工程学院, 山东 烟台 264005
Author(s):
GAO Xueyi12 ZHANG Nan12 TONG Xiangrong12 JIANG Lili12
1. Key Lab for Data Science and Intelligent Technology of Shandong Higher Education Institutes, Yantai University, Yantai 264005, China;
2. School of Computer and Control Engineering, Yantai University, Yantai 264005, China
关键词:
分布保持属性约简粗糙集概率分布差别矩阵
Keywords:
distribution preservationattribute reductionrough setsprobability distributiondiscernibility matrix
分类号:
TP181
DOI:
10.11992/tis.201704025
摘要:
属性约简是粗糙集理论的重要研究内容之一。分布约简保证约简前后每个对象的概率分布保持不变,即保证每条规则的置信度在约简前后不发生改变。实际应用中,人们往往更加关注可信度较高或较低的规则。因此,在本文中引入了广义分布保持属性约简,该属性约简可以保证规则的置信度PP∈[0,α]或[β,1])在约简前后不变。同时,给出了广义分布保持属性约简的判定方法与基于差别矩阵的广义分布保持属性约简算法,深入讨论了几种特殊情形下的广义分布保持约简。最后,在4个UCI数据集上进行的实验分析表明,几种特殊情形下的广义分布保持属性约简可退化为已有的一些属性约简,且在不同置信区间下求得的广义分布保持属性约简存在包含关系,验证了相关结论的正确性。
Abstract:
Attribute reduction is a pertinent issue in rough set theory. Distribution reduction ensures that the probability distribution of each target does not change before and after reduction; i.e., it ensures that the confidence of every rule remains unchanged before and after reduction. In actual applications, people are often interested in rules that have higher or lower confidences. Thus, attribute reduction based on generalized distribution preservation is proposed in this paper. Confidences in [0, α] or [β, 1] were unchanged using the proposed technique. We also propose judgment methods for generalized-distribution-preservation attribute reduction and investigate the generalized attribute-reduction algorithm based on a discernibility matrix. Some special cases with respect to generalized-distribution-preservation attribute reduction are discussed in depth. Finally, experiments on four data sets downloaded from UCI show that some special cases with respect to generalized distribution preservation reduction could degenerate into some existing attribute reductions and inclusion relations exist in generalized distribution preservation attribute reduction under different confidence intervals, verifying the correctness of the relevant conclusions.

参考文献/References:

[1] PAWLAK Z. Rough sets[J]. International journal of com-puter and information sciences, 1982, 11(5): 341-356.
[2] PAWLAK Z. Rough sets: theoretical aspects of reasoning about data[M]. Boston:Kluwer Academic Publishers, 1992.
[3] 张文修. 粗糙集理论与方法[M]. 北京:科学出版社, 2001.
[4] 王国胤, 姚一豫, 于洪. 粗糙集理论与应用研究综述[J]. 计算机学报, 2009, 32(7): 1229-1246. WANG Guoyin, YAO Yiyu, YU Hong. A survey on rough set theory and applications[J]. Chinese journal of computers, 2009, 32(7): 1229-1246.
[5] SKOWRON A, RAUSZER C. The discernibility matrices and functions in information systems[J]. Theory and decision library, 1992, 11: 331-362.
[6] KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information sciences, 1998, 112 (1/2/3/4): 39-49.
[7] 张文修, 米据生, 吴伟志. 不协调目标信息系统的知识约简[J]. 计算机学报, 2003, 26(1): 12-18.ZHANG Wenxiu, MI Jusheng, WU Weizhi. Knowledge reductions in inconsistent information systems[J]. Chinese journal of computers, 2003, 26(1): 12-18.
[8] 徐伟华, 张文修. 基于优势关系下不协调目标信息系统的分布约简[J]. 模糊系统与数学, 2007, 21(4): 124-131.XU Weihua, ZHANG Wenxiu. Distribution reduction in inconsistent information systems based on dominance relations[J]. Fuzzy systems and mathematics, 2007, 21(4):124-131.
[9] MIAO Duoqian, ZHAO Yan, YAO Yiyu, et al. Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model[J]. Information sciences, 2009, 179(24): 4140-4150.
[10] 张楠, 苗夺谦, 岳晓冬. 区间值信息系统的知识约简[J]. 计算机研究与发展, 2010, 47(8): 1362-1371.ZHANG Nan, MIAO Duoqian, YUE Xiaodong. Approaches to knowledge reduction in interval-valued information systems[J]. Journal of computer research and development, 2010, 47(8): 1362-1371.
[11] 苗夺谦, 胡桂荣. 知识约简的一种启发式算法[J]. 计算机研究与发展, 1999, 36(6): 681-684.MIAO Duoqian, HU Guirong. A heuristic algorithm for reduction of knowledge[J]. Journal of computer research and development, 1999, 36(6): 681-684.
[12] 王国胤, 于洪, 杨大春. 基于条件信息熵的决策表约简[J]. 计算机学报, 2002, 25(7): 759-766.WANG Guoyin, YU Hong, YANG Dachun. Decision table reduction based on conditional information entropy[J]. Chinese journal of computers, 2002, 25(7): 759-766.
[13] QIAN Yuhua, LIANG Jiye, PEDRYCZ W, et al. Positive approximation: an accelerator for attribute reduction in rough set theory[J]. Artificial intelligence, 2010, 174(9): 597-618.
[14] QIAN Yuhua, LIANG Jiye, PEDRYCZ W, et al. An efficient accelerator for attribute reduction from incom-plete data in rough set framework[J]. Pattern recognition, 2011, 44(8): 1658-1670.
[15] 钱宇华, 梁吉业, 王锋. 面向非完备决策表的正向近似特征选择加速算法[J]. 计算机学报, 2011, 34(3): 435-442.QIAN Yuhua, LIANG Jiye, WANG Feng. A positive approximation based accelerated algorithm to feature selection from incomplete decision tables[J]. Chinese journal of computers, 2011, 34(3): 435-442.
[16] CHEN Hongmei, LI Tianrui, RUAN Da, et al. A rough-set based incremental approach for updating approximations under dynamic maintenance environments[J]. IEEE transactions on knowledge and data engineering, 2013, 25(2): 274-284.
[17] CHEN Hongmei, LI Tianrui, LUO Chuan, et al. A rough set-based method for updating decision rules on attribute values’ coarsening and refining[J]. IEEE transactions on knowledge and data engineering, 2014, 26(12): 2866-2899.
[18] JIA Xiuyi, SHANG Lin, ZHOU Bing, et al. Generalized attribute reduct in rough set theory[J]. Knowledge-based systems, 2015, 91: 204-218.
[19] ZHOU Jie, MIAO Duoqian, PEDRYCZ W, et al. Analysis of alternative objective functions for attribute reduction in complete decision tables[J]. Soft computing, 2011, 15(8): 1601-1616.
[20] ZHANG Xiao, MEI Changlin, CHEN Degang, et al. Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems[J]. International journal of approximate reasoning, 2014, 55(8): 1787-1804.

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备注/Memo

备注/Memo:
收稿日期:2017-04-19。
基金项目:国家自然科学基金项目(61403329,61572418,61502410,61572419);山东省自然科学基金项目(ZR2013FQ020,ZR2015PF 010);山东省高等学校科技计划项目(J15LN09,116LN17).
作者简介:高学义,男,1992年生,硕士研究生,主要研究方向为粗糙集、数据挖掘与机器学习;张楠,男,1979年生,博士,主要研究方向为粗糙集、认知信息学与人工智能;童向荣,男,1975年生,教授,博士,主要研究方向为多Agent系统、分布式人工智能与数据挖掘技术。
通讯作者:张楠.E-mail:zhangnan0851@163.com.
更新日期/Last Update: 2017-06-25