[1] ZADEH L A, GUPTA M M, RAGADE R K, et al. Fuzzy sets and information granularity[M]//Advances in fuzzy set theory and applications. [S.l.]: North Holland Publishing Company, 1979: 3-18.
[2] LIN T Y. Neighborhood systems and relational databases[C]//Proceedings of the 1988 ACM Sixteenth Annual Conference on Computer Science, 1988: 725.
[3] LIN T Y, ZADEH L A. Special issue on granular computing and data mining[J]. International Journal of Intelligent Systems, 2004, 19(7): 565-566.
[4] YAO Y Y. Information granulation and rough set approximation[J]. International Journal of Intelligent Systems, 2001, 16(1): 87-104.
[5] YAO Y Y. Relational interpretations of neighborhood operators and rough set approximation operators[J]. Information Sciences, 1998, 111(1/4): 239-259.
[6] 苗夺谦, 范世栋. 知识的粒度计算及其应用[J]. 系统工程理论与实践, 2002, 22(1): 48-56.
MIAO D Q, FAN S D. The calculation of knowledge granulation and its application[J]. Systems Engineering-Theory & Practice, 2002, 22(1): 48-56.
[7] 胡清华, 于达仁, 谢宗霞. 基于邻域粒化和粗糙逼近的数值属性约简[J]. 软件学报, 2008, 19(3): 640-649.
HU Q H, YU D R, XIE Z X. Numerical attribute reduction based on neighborhood granulation and rough approximation[J]. Journal of Software, 2008, 19(3): 640-649.
[8] HU Q H, YU D R, XIE Z X Neighborhood classifiers[J]. Expert Systems with Applications, 2008, 34(2): 866-876.
[9] ZHU P F, HU Q H, HAN Y H, et al. Combining neighborhood separable subspaces for classification via sparsity regularized optimization[J]. Information Sciences, 2016, 370-371(1): 270-287.
[10] CHEN Y, ZHU S, LI W, et al. Fuzzy granular convolutional classifiers[J]. Fuzzy Sets and Systems, 2022, 426: 145-162.
[11] LI W, MA X Y, CHEN Y M, et al. Random fuzzy granular decision tree[J]. Mathematical Problems in Engineering, 2021(10): 1-17.
[12] CHEN Y, QIN N, LI W, et al. Granule structures, distances and measures in neighborhood systems[J]. Knowledge-Based Systems, 2019, 165: 268-281.
[13] 卜东波, 白硕, 李国杰. 聚类/分类中的粒度原理[J]. 计算机学报, 2002, 25(8): 810-816.
BU D B, BAI S, LI G J. Principle of granularity in clustering/classification[J]. Chinese Journal of Computers, 2002, 25(8): 810-816.
[14] 王国胤, 张清华, 胡军. 粒计算研究综述[J]. 智能系统学报, 2007, 2(6): 8-26.
WANG G Y, ZHANG Q H, HU J. An overview of granular computing[J]. CAAI Transactions on Intelligent Systems, 2007, 2(6): 8-26.
[15] DAI J, HAN H, ZHANG X, et al. Catoptrical rough set model on two universes using granule-based definition and its variable precision extensions[J]. Information Sciences, 2017(390): 70-81.
[16] ROH S B, OH S K, PEDRYCZ W. A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering[J]. Knowledge-Based Systems, 2010, 23(3): 202-219.
[17] WU W Z, LEUNG Y. Theory and applications of granular labelled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181(18): 3878-3897.
[18] BREIMAN L. Random forest[J]. Machine Learning, 2001, 45: 5-32.
[19] 任家东, 刘新倩, 王倩, 等. 基于KNN离群点检测和随机森林的多层入侵检测方法[J]. 计算机研究与发展, 2019, 56(3): 566-575.
REN J D, LIU X Q, WANG Q, et alL. A multi-level intrusion detection method based on KNN outlier detection and random forests[J]. Journal of Computer Research and Development, 2019, 56(3): 566-575.
[20] 沈晶磊, 虞慧群, 范贵生, 等. 基于随机森林算法的推荐系统的设计与实现[J]. 计算机科学, 2017, 44(11): 164-167.
SHEN J L, YU H Q, FAN G S, et al. Design and implementation of a recommendation system based on random forest algorithm[J]. Computer Science, 2017, 44(11): 164-167.
[21] 李旭明, 李传军. 基于随机森林模型的输电线路故障检测系统研究[J]. 计算技术与自动化, 2020, 39(1): 29-33.
LI X M, LI C J. Research on transmission line fault detection system based on random forest model[J]. Computing Technology and Automation, 2020, 39(1): 29-33.
[22] 方匡南, 吴见彬, 朱建平, 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011, 26(3): 32-38.
FANG K N, WU J B, ZHU J P, et al. A review of random forest method[J]. Statistics and Information Forum, 2011, 26(3): 32-38.
[23] 徐计, 王国胤, 于洪. 基于粒计算的大数据处理[J]. 计算机学报, 2015, 38(8): 1497-1517.
XU J, WANG G Y, YU H, Big data processing based on granular computing[J]. Chinese Journal of Computers, 2015, 38(8): 1497-1517.
[24] GEURTS P, ERNST D, WEHENKEL L. Extremely randomized trees[J]. Machine Learning, 2006, 63(1): 3-42.
[25] RODRíGUEZ D, JUAN J, LUDMILA I. K. , et al, Rotation forest: a new classifier ensemble method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28: 1619-1630.
[26] CHEN T, GUESTRIN C. Xgboost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 785-794. |