计算机科学 ›› 2015, Vol. 42 ›› Issue (5): 265-269.doi: 10.11896/j.issn.1002-137X.2015.05.053
焦 娜
JIAO Na
摘要： 有效的知识约简算法是粗糙集理论的重要研究内容。粗糙集是一个去掉冗余特征的有效工具。经典的粗糙集方法要求数值用离散数据表达,对于连续值则在处理前必须进行离散化处理。真实数据往往存在连续值,为了避免运用粗糙集方法所必需的离散化过程带来的信息丢失,将差异关系应用于粗糙集的知识约简。为进一步增强差异关系粗糙集对噪声数据的适应能力,提出基于差异关系的变精度粗糙集知识约简算法,并分析差异关系下变精度粗糙集模型参数的特性,给出依赖度和参数范围关系描述,将参数取值从点扩展到区间范围。在UCI数据库的数据集上进行实验,结果证明了所提方法及相关理论的有效性。
| [1] Pawlak Z.Rough sets[J].International Journal of Information Computer Science,1982,11(5):341-356 [2] Ziarko W.Variable precision rough set model[J].Journal ofComputer and System Sciences,1993,46:39-59 [3] Katzberg J D,Ziarko W.Variable precision rough sets withasymmetric bounds[C]∥Ziarko W.ed.Proceedings of Rough Sets,and Fuzzy Sets and Knowledge Discovery(RSKD’93).London:Springer-Verlag,1994:167-176 [4] Mi J S,Wu W Z,Zhang W X.Approaches to knowledge reduction based on variable precision rough set model[J].Information Sciences,2004,159(3):255-272 [5] 张贤勇,莫智文.变精度粗糙集[J].模式识别与人工智能,2004,17(2):151-155 [6] Zhang X Y,Mo Z W,Xiong F,et al.Comparative study of variable precision rough set model and graded rough set model[J].International Journal of Approximate Reasoning,2012,53(1):104-116 [7] Zhang H Y,Leung Y,Zhou L.Variable-precision-dominance-based rough set approach to interval-valued information systems[J].Information Sciences,2013,244(20):75-272 [8] Yao Y Y.Probabilistic rough set approximations[J].International Journal of Approximate Reasoning,2008,49(2):255-271 [9] Yao Y Y,Yao B X.Covering based rough set approximations[J].Information Sciences,2012,200:91-107 [10] 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 [11] Yang X B,Xie J,Song X N,et al.Credible rules in incomplete decision system based on descriptors[J].Knowledge-Based Systems,2009,22:8-17 [12] Yu Y,Pedrycz W,Miao D Q.Neighborhood rough sets based multi-label classification for automatic image annotation[J].Journal of Approximate Reasoning,2013,54(9):1373-1387 [13] 王国胤.Rough集理论与知识获取[M].西安:西安交通大学出版社,2001 [14] 苗夺谦.粗糙集理论中连续属性的离散化方法[J].自动化学报,2001,27(3):296-302 [15] Jensen R,Shen Q.Tolerance-based and fuzzy-rough feature selection[C]∥Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ- IEEE’07).2007:877-882 [16] Parthaláin N M,Shen Q.Exploring the boundary region of to-lerance rough sets for feature selection[J].Pattern Recognition,2009,42:655-667 [17] Shen Q,Chouchoulas A.A rough-fuzzy approach for generating classification rules[J].Pattern Recognition,2002,5:2425-2438 [18] Grzymala-Busse J W.Discretization of numerical attributes[M]∥Klsgen W,Zytkow J,eds.Handbook of Data Mining and Knowledge Discovery.Oxford University Press,2002:218-225 [19] Grzymala-Busse J W,Grzymala-Busse W J.Handling missing attribute values[M]∥ Maimon O,Rokach L,eds.Handbook of Data Mining and Knowledge Discovery.2005:37-57 [20] Min F,Zhu W.Attribute reduction of data with error ranges and test costs[J].Information Sciences,2012,211:48-67 [21] Zhao H,Min F,Zhu W.Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors[J].Journal of Applied Mathematics,2013,2013 |
| No related articles found! |
|
||
首页