[1]翟俊海,刘博,张素芳.基于粗糙集相对分类信息熵和粒子群优化的特征选择方法[J].智能系统学报,2017,12(3):397-404.[doi:10.11992/tis.201705004]
 ZHAI Junhai,LIU Bo,ZHANG Sufang.A feature selection approach based on rough set relative classification information entropy and particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2017,12(3):397-404.[doi:10.11992/tis.201705004]
点击复制

基于粗糙集相对分类信息熵和粒子群优化的特征选择方法

参考文献/References:
[1] GUYON I, GUNN S, NIKRAVESH M, et al. Feature extraction, foundations and applications[M]. Berlin: Springer, 2006.
[2] DASH M, LIU H. Feature selection for classification [J]. Intelligent data analysis, 1997, 1: 131-151.
[3] PAWLAK Z. Rough sets [J]. Internationa journal of information and computer sciences, 1982, 11: 341-356.
[4] 苗夺谦, 李道国. 粗糙集理论、算法与应用 [M]. 北京: 清华大学出版社, 2008.
[5] SWINIARSKI R W, SKOWRON A. Rough set methods in feature selection and recognition[J]. Pattern recognition letters, 2003, 24(6): 833-849.
[6] JENSEN R, SHEN Q. Fuzzy-rough sets for descriptive dimensionality reduction[C]//IEEE International Conference on Fuzzy Systems, 2002. Fuzz-IEEE. 2002:29-34.
[7] BHATT R B, GOPAL M. On fuzzy-rough sets approach to feature selection[J]. Pattern recognition letters, 2005, 26(7): 965-975.
[8] JENSEN R, PARTHALáIN N M. Towards scalable fuzzy rough feature selection[J]. Information sciences, 2015, 323(C): 1-15.
[9] QIAN Y H, LIANG J, PEDRYCZ W, et al. Positive approximation: an accelerator for attribute reduction in rough set theory[J]. Artificial intelligence, 2010, 174(9/10): 597-618.
[10] HU Q H, YU D R, LIU J F, et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information sciences, 2008, 178(18): 3577-3594.
[11] ALMUALLIM H, DIETTERICH T G. Learning boolean concepts in the presence of many irrelevant features[J]. Artificial intelligence, 1994, 69 (1/2): 279-305.
[12] DASH M, LIU H. Consistency-based search in feature selection[J]. Artificial intelligence 2003 (151):155-176.
[13] BATTITI R. Using mutual information for selecting features in supervised neural net learning[J]. IEEE transactions on neural networks, 1994, 5(4): 537-549.
[14] KWAK N, CHOI C H. Input feature selection by mutual information based on parzen window [J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(12): 1667-1671.
[15] ESTEVEZ P A, TESMER M, PEREZ C A, et al. Normalized mutual information feature selection [J]. IEEE transactions on neural networks, 2009, 20(2): 189-201.
[16] SONG L, SMOLA A, GRETTON A, et al. Feature selection via dependence maximization [J]. Journal of machine learning research, 2012, 13:1393-1434.
[17] HU Q H, ZHU Pengfei, LIU Jinfu, et al. Feature selection via maximizing fuzzy dependency[J]. Fundamenta informaticae, 2010, 98: 167-181.
[18] KOHAVI R, JOHN G. Wrappers for feature subset selection[J]. Artificial intelligence, 1997, 97(1/2): 273-324.
[19] SINDHWANI V, RAKSHIT S, DEODHARE D, et al. Feature selection in MLPs and SVMs based on maximum output information[J]. IEEE transactions on neural networks, 2004, 15(4): 937-947.
[20] YANG Jianbo, SHEN Kaiquan, ONG Chongjin, et al. Feature selection for MLP neural network: the use of random permutation of probabilistic outputs[J]. IEEE transactions on neural networks, 2009, 20(12): 1911-1922.
[21] QUINLAN J R. Induction of decision trees [J]. Machine learning, 1986, 1: 81-106.
[22] BREIMAN L, FRIEDMAN J H, RICHARD A S, et al. Classification and regression trees[M]. Belmont, CA: wadsworth international group, 1984.
[23] SETIONO R, LIU H. Neural-network feature selector [J]. IEEE transactions on neural networks, 1997, 8(3): 654-662.
[24] SHEN Kaiquan, ONG Chongjin, LI Xiaoping, et al. Feature selection via sensitivity analysis of SVM probabilistic outputs[J]. Machine learning, 2008, 70: 1-20.
[25] PERKINS S, LACKER K, THEILER J. Grafting: fast, incremental feature selection by gradient descent in function space [J]. Journal of machine learning research, 2003 (3) : 1333-1356.
[26] KENNEDY J, EBERHART R. Particle swarm optimization [C]. IEEE International Conference on Neural Networks. Perth, Australia, 1995, 4: 1942-1948.
[27] EBERHART R C, SHI Y H, KENNEDY J. Swarm Intelligence[M]. Massachusetts: Morgan Kaufmann, 2001.
[28] EBERHART R C, KENNEDY J. A discrete binary version of the particle swarm algorithm [J].IEEE conference on systems, 1997, 5: 4104-4109.
[29] CHUANG L Y, CHANG H W, TU C J, et al. Improved binary PSO for feature selection using gene expression data[J]. Computational biology & chemistry, 2008, 32(1): 29-37.
[30] CHUANG L Y, TSAI S W, YANG C H. Improved binary particle swarm optimization using catfish effect for feature selection[J]. Expert systems with applications, 2011, 38(10): 12699-12707.
[31] WANG Xiangyang, YANG Jie, TENG Xiaolong, et al. Feature selection based on rough sets and particle swarm optimization[J]. Pattern recognition letters, 2007, 28(4): 459-471.
[32] CERVANTE L, XUE B, ZHANG M, et al. Binary particle swarm optimisation for feature selection: a filter based approach[J]. Evolutionary computation, 2012, 41: 1-8.
[33] LIU Quanjin, ZHAO Zhimin, LI Yingxin. Ensemble feature selection method based on neighborhood information and PSO algorithm[J]. Acta electronica sinica, 2016, 44(4): 995-1002.
[34] FONG S, WONG R, VASILAKOS A. Accelerated PSO swarm search feature selection for data stream mining big data[J]. IEEE transactions on services computing, 2016, 9(1): 33-45.
[35] 翟俊海, 刘博, 张素芳. 基于相对分类信息熵的进化特征选择算法[J]. 模式识别与人工智能, 2016, 29(8):682-690.ZHAI Junhai, LIU Bo, ZHANG Sufang. Feature selection via evolutionary computation based on relative classification information entropy[J]. Pattern recognition and artificial intelligence, 2016, 29(8): 682-690.
[36] SHI B Y, EBERHART R. A modified particle swarm optimizer[J]. IEEE world congress on computational intelligence, 1999, 6: 69-73.
相似文献/References:
[1]张继福,张素兰,胡立华.约束概念格及其构造方法[J].智能系统学报,2006,1(2):31.
 ZHANG Ji-fu,ZHANG Su-lan,HU Li-hua.Constrained concept lattice and its construction method[J].CAAI Transactions on Intelligent Systems,2006,1():31.
[2]孙正兴,张尧烨,李? 彬.基于线性规划分类器的相关反馈技术[J].智能系统学报,2007,2(3):34.
 SUN Zheng-xing,ZHANG Yao-ye,LI Bin.Applying relevance feedback with a linear programming classifier[J].CAAI Transactions on Intelligent Systems,2007,2():34.
[3]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.
 WANG Guo-yin,ZHANG Qing-hua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,2():8.
[4]张志飞,苗夺谦.基于粗糙集的文本分类特征选择算法[J].智能系统学报,2009,4(5):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
 ZHANG Zhi-fei,MIAO Duo-qian.Feature selection for text categorization based on rough set[J].CAAI Transactions on Intelligent Systems,2009,4():453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
[5]顾成杰,张顺颐,杜安源.结合粗糙集和禁忌搜索的网络流量特征选择[J].智能系统学报,2011,6(3):254.
 GU Chengjie,ZHANG Shunyi,DU Anyuan.Feature selection of network traffic using a rough set and tabu search[J].CAAI Transactions on Intelligent Systems,2011,6():254.
[6]何清.物联网与数据挖掘云服务[J].智能系统学报,2012,7(3):189.
 HE Qing.The Internet of things and the data mining cloud service[J].CAAI Transactions on Intelligent Systems,2012,7():189.
[7]孙倩茹,王文敏,刘宏.视频序列的人体运动描述方法综述[J].智能系统学报,2013,8(3):189.
 SUN Qianru,WANG Wenmin,LIU Hong.Study of human action representation in video sequences[J].CAAI Transactions on Intelligent Systems,2013,8():189.
[8]曹晋,张莉,李凡长.一种基于支持向量数据描述的特征选择算法[J].智能系统学报,2015,10(2):215.[doi:10.3969/j.issn.1673-4785.201405063]
 CAO Jin,ZHANG Li,LI Fanzhang.A noval support vector data description-based feature selection method[J].CAAI Transactions on Intelligent Systems,2015,10():215.[doi:10.3969/j.issn.1673-4785.201405063]
[9]李海林,郭韧,万校基.基于特征矩阵的多元时间序列最小距离度量方法[J].智能系统学报,2015,10(3):442.[doi:10.3969/j.issn.1673-4785.201405047]
 LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10():442.[doi:10.3969/j.issn.1673-4785.201405047]
[10]张佳骕,蒋亦樟,王士同.基于特征选择聚类方法的稀疏TSK模糊系统[J].智能系统学报,2015,10(4):583.[doi:10.3969/j.issn.1673-4785.201412001]
 ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,10():583.[doi:10.3969/j.issn.1673-4785.201412001]

备注/Memo

收稿日期:2017-05-07。
基金项目:国家自然科学基金项目(71371063);河北省自然科学基金项目(F2017201026);浙江省计算机科学与技术重中之重学科(浙江师范大学)资助项目.
作者简介:翟俊海,男,1964年生,男,教授,中国人工智能学会粗糙集与软计算专业委员会委员,主要研究方向为机器学习。近几年主持或参与省部级以上项目10余项,获河北省自然科学三等奖1项,出版专著4部,发表论文70余篇;刘博,男,1989年生,硕士研究生,主要研究方向为机器学习;张素芳,女,1966年生,副教授,主要研究方向为机器学习。
通讯作者:翟俊海.E-mail:mczjh@126.com.

更新日期/Last Update: 2017-06-25
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134