自适应的邻域粗糙集邻域大小取值方法
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摘要
邻域粗糙集应用的好坏依赖于邻域大小δ的取值。在使用基于邻域粗糙集的属性约简算法时,现有的δ取值方法一般是点值式的,即仅凭借人的经验指定某个值,这种方法在对δ取值时没有结合实际问题的具体情况,因此在算法的实用性上可以作进一步讨论。为此,提出一种自适应δ取值方法,其最大特点是不指定δ取值,而是指定δ取值的区间,然后在该取值区间上,通过使用一种结合了数据集和分类器自身特性的适应值函数自动地选出最合适的δ取值。实验结果表明,相比点值式δ取值方法,通过自适应δ取值方法能找到属性个数更少,而分类精度更高的属性集。实验证明该方法能进一步提高基于邻域粗糙集的属性约简算法的实用性。
The application of neighborhood rough set depends on the value of neighborhood size δ. When using attribute reduction algorithms based on neighborhood rough set,an existing method for determining δ is usually point-type,that is,to specify a value only by human experience. The method does not combine with the actual situation when it is used to determineδ,so the practicability of the algorithms can be further discussed. For this reason,this paper proposed an adaptable method for determining δ,which the biggest characteristic was not determining δ but the interval of δ. It forwardly selected the most appropriate δ in the interval by using a fitness function that was combined with the characteristics of data sets and classifiers. The experimental results show that,compared with the point-type method for determining δ,this method can find reduction sets which number of attributes is less,and classification accuracy is higher. It proves that this method can further improve the practicability of attribute reduction algorithms based on neighborhood rough set.
The application of neighborhood rough set depends on the value of neighborhood size δ. When using attribute reduction algorithms based on neighborhood rough set,an existing method for determining δ is usually point-type,that is,to specify a value only by human experience. The method does not combine with the actual situation when it is used to determineδ,so the practicability of the algorithms can be further discussed. For this reason,this paper proposed an adaptable method for determining δ,which the biggest characteristic was not determining δ but the interval of δ. It forwardly selected the most appropriate δ in the interval by using a fitness function that was combined with the characteristics of data sets and classifiers. The experimental results show that,compared with the point-type method for determining δ,this method can find reduction sets which number of attributes is less,and classification accuracy is higher. It proves that this method can further improve the practicability of attribute reduction algorithms based on neighborhood rough set.
引文
[1]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.)
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[9]刘遵仁,吴耿锋.基于邻域粗糙模型的高维数据集快速约简算法[J].计算机科学,2012,39(10):268-271,317.(Liu Zunren,Wu Gengfeng. Quick reduction algorithm for high-dimensional data sets based on neighborhood rough set model[J]. Computer Science,2012,39(10):268-271,317.)
[10]Liu Yong,Huang Wenliang,Jiang Yunliang,et al. Quick attribute reduct algorithm for neighborhood rough set model[J]. Information Sciences,2014,271(7):65-81.
[11]段洁,胡清华,张灵均,等.基于邻域粗糙集的多标记分类特征选择算法[J].计算机研究与发展,2015,52(1):56-65.(Duan Jie,Hu Qinghua,Zhang Lingjun,et al. Feature selection for multi-label classification based on neighborhood rough sets[J]. Journal of Computer Research and Development,2015,52(1):56-65.)
[12] Chen Hongmei,Li Tianrui,Luo Chuan,et al. Dominance-based neighborhood rough sets and its attribute reduction[C]//Rough Sets and Knowledge Technology. Cham:Springer,2015:89-99.
[13]Guo Gongzhen,Liu Zunren,Lou Chang,et al. Improving on a rapid attribute reduction algorithm based on neighborhood rough sets[C]//Proc of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Piscataway,NJ:IEEE Press,2016:236-240.
[14]Wang Changzhong,Shao Mingwen,He Qiang,et al. Feature subset selection based on fuzzy neighborhood rough sets[J]. KnowledgeBased Systems,2016,111(11):173-179.
[15]Chen Yumin,Zeng Zhiqiang,Lu Junwen. Neighborhood rough set reduction with fish swarm algorithm[J]. Soft Computing,2017,21(23):6907-6918.
[16]Fan Anjing,Zhao Hong,Zhu W. Test-cost-sensitive attribute reduction on heterogeneous data for adaptive neighborhood model[J]. Soft Computing,2016,20(12):4813-4824.
[2] Pawlak Z,Sowinski R. Rough set approach to multi-attribute decision analysis[J]. European Journal of Operational Research,1994,72(3):443-459.
[3] Zadeh L A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets&Systems,1997,90(2):111-127.
[4] Lin T Y. Granular computing on binary relations I:data mining and neighborhood systems[M]//Rough Sets in Knowledge Discovery.Heidelberg:Physica-Verlag,1998:107-121.
[5] Hu Qinghua,Yu Daren,Liu Jinfu,et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Sciences,2008,178(18):3577-3594.
[6]王国胤. Rough集理论与知识获取[M].西安:西安交通大学出版社,2001:147-156.(Wang Guoyin. Rough set theory and knowledge acquisition[M]. Xi’an:Xi’an Jiaotong University Press,2001:147-156.)
[7]胡清华,于达人.应用粗糙计算[M].北京:科学出版社,2012.(Hu Qinghua,Yu Daren. Application of rough computing[M]. Beijing:Science Press,2012.)
[8] Du Yong,Hu Qinghua,Zhu Pengfei,et al. Rule learning for classification based on neighborhood covering reduction[J]. Information Sciences,2011,181(24):5457-5467.
[9]刘遵仁,吴耿锋.基于邻域粗糙模型的高维数据集快速约简算法[J].计算机科学,2012,39(10):268-271,317.(Liu Zunren,Wu Gengfeng. Quick reduction algorithm for high-dimensional data sets based on neighborhood rough set model[J]. Computer Science,2012,39(10):268-271,317.)
[10]Liu Yong,Huang Wenliang,Jiang Yunliang,et al. Quick attribute reduct algorithm for neighborhood rough set model[J]. Information Sciences,2014,271(7):65-81.
[11]段洁,胡清华,张灵均,等.基于邻域粗糙集的多标记分类特征选择算法[J].计算机研究与发展,2015,52(1):56-65.(Duan Jie,Hu Qinghua,Zhang Lingjun,et al. Feature selection for multi-label classification based on neighborhood rough sets[J]. Journal of Computer Research and Development,2015,52(1):56-65.)
[12] Chen Hongmei,Li Tianrui,Luo Chuan,et al. Dominance-based neighborhood rough sets and its attribute reduction[C]//Rough Sets and Knowledge Technology. Cham:Springer,2015:89-99.
[13]Guo Gongzhen,Liu Zunren,Lou Chang,et al. Improving on a rapid attribute reduction algorithm based on neighborhood rough sets[C]//Proc of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Piscataway,NJ:IEEE Press,2016:236-240.
[14]Wang Changzhong,Shao Mingwen,He Qiang,et al. Feature subset selection based on fuzzy neighborhood rough sets[J]. KnowledgeBased Systems,2016,111(11):173-179.
[15]Chen Yumin,Zeng Zhiqiang,Lu Junwen. Neighborhood rough set reduction with fish swarm algorithm[J]. Soft Computing,2017,21(23):6907-6918.
[16]Fan Anjing,Zhao Hong,Zhu W. Test-cost-sensitive attribute reduction on heterogeneous data for adaptive neighborhood model[J]. Soft Computing,2016,20(12):4813-4824.