基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法

doi:10.16451/j.cnki.issn1003-6059.202002001

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (805 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对数据降维和去冗问题,提出基于改进的二元萤火虫群优化算法和邻域粗糙集的属性约简方法.首先,运用反向学习协同初始化种群,并基于Sigmoid变化函数的映射进行二进制编码,引入Lévy飞行位置更新策略,提出改进二元萤火虫群优化算法.再以邻域粗糙集作为评价准则,以改进算法作为搜索策略,进行属性约简.最后,通过在标准UCI数据集上的实验验证属性约简方法的有效性,并验证文中算法具有较优的收敛速度和精度.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	彭鹏
	倪志伟
	朱旭辉
	夏平凡

关键词 ：属性约简, 邻域粗糙集, 二元萤火虫群优化算法, 反向学习, Lévy飞行

Abstract：Aiming at the problems of dimension reduction and redundancy removing, an attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set is proposed. Firstly, the population is collaborative initialization using reverse learning, and the mapping of the change function based on Sigmoid is employed for binary coding, and an improved binary glowworm opti-mization algorithm is proposed with Lévy flight position update strategy. Secondly, neighborhood rough set is employed as an evaluation criterion, and the proposed algorithm is utilized as an search strategy for attribute reduction. Finally, experiments on the standard UCI datasets demonstrate the effectiveness of the attribute reduction method, and the better convergence speed and accuracy of the proposed algorithm is verified.

Key words： Attribute Reduction Neighborhood Rough Set Binary Glowworm Swarm Optimization Algorithm Reverse Learning Lévy Flight

收稿日期: 2019-06-27

ZTFLH:

TP 391

基金资助:国家自然科学基金项目(No.71490725,71521001,91546108)、国家自然科学基金青年项目(No.71701061)、安徽省自然科学基金项目(No.1908085QG298)、中央高校基本科研业务费专项资金项目(No.JZ2019HGTA0053,JZ2019HGBZ0128)资助

通讯作者: 倪志伟,博士,教授,主要研究方向为人工智能、机器学习、云计算.E-mail:zhwnelson@163.com.

作者简介: 彭鹏,博士研究生,讲师,主要研究方向为数据挖掘、智能优化.E-mail:454472870@qq.com.朱旭辉,博士,讲师,主要研究方向为智能计算、机器学习.E-mail:zhuxuhui@hfut.edu.cn.夏平凡,博士研究生,主要研究方向为智能计算、机器学习、互联网金融.E-mail:xiapingfan@mail.hfut.edu.cn.

引用本文:

彭鹏, 倪志伟, 朱旭辉, 夏平凡. 基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法[J]. 模式识别与人工智能, 2020, 33(2): 95-105. PENG Peng, NI Zhiwei, ZHU Xuhui, XIA Pingfan. Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set. , 2020, 33(2): 95-105.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202002001 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2020/V33/I2/95

[1] JR TRAINA C, TRAINA A, WU L, et al. Fast Feature Selection Using Fractal Dimension. Journal of Information and Data Management, 2000, 1(1): 158-171.
[2] 倪志伟,肖宏旺,伍章俊,等.基于改进离散型萤火虫群优化算法和分形维数的属性选择方法.模式识别与人工智能, 2013, 26(12): 1169-1178.
(NI Z W, XIAO H W, WU Z J, et al. Attribute Selection Method Based on Improved Discrete Glowworm Swarm Optimization and Fractal Dimension. Pattern Recognition and Artificial Intelligence, 2013, 26(12): 1169-1178.)
[3] 朱旭辉,倪志伟,程美英,等.融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法.系统工程理论与实践, 2017, 37(4): 999-1010.
(ZHU X H, NI Z W, CHENG M Y, et al. Haze Prediction Method Based on Multi-fractal Dimension and Co-evolution Discrete Artificial Fish Swarm Algorithm. Systems Engineering-Theory and Practice, 2017, 37(4): 999-1010.)
[4] 吴尚智,罗艺纯,翟敬鹏.基于遗传粒子群和粗糙集的最小属性约简算法.计算机工程与科学, 2016, 38(5): 1007-1013.
(WU S Z, LUO Y C, ZHAI J P. A Minimum Attribute Reduction Algorithm Based on Genetic and Particle Swarm Optimization and Rough Sets. Computer Engineering and Science, 2016, 38(5): 1007-1013.)
[5] 刘小伟,王宁,李天瑞,等.概率复合粗糙集模型的改进及其属性约简.南京大学学报(自然科学), 2018, 54(5): 958-966.
(LIU X W, WANG N, LI T R, et al. Improved Probabilistic Composite Rough Set Model and Attribute Reduction. Journal of Nanjing University(Nature Science), 2018, 54(5): 958-966.)
[6] 孟宏鹏,徐海燕,宋遐淦.基于粗糙集属性约简和支持向量机的变压器故障诊断.南京航空航天大学学报, 2017, 49(4): 504-510.
(MENG H P, XU H Y, SONG X G. Transformer Fault Diagnosis Based on Attribute Reduction of Rough Set and SVM. Journal of Nanjing University of Aeronautics and Astronautics, 2017, 49(4): 504-510.)
[7] YUAN Q, TANG H B, YOU W, et al. Virtual Network Function Scheduling via Multilayer Encoding Genetic Algorithm with Distributed Bandwidth Allocation. Science China(Information Science), 2018, 61(9): 092107:1-092107:19.
[8] DU Y B, XIAO L F, CHEN Y S, et al. Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM. Transactions of Nanjing University of Aeronautics and Astronautics, 2018, 35(2): 334-342.
[9] ARAFAT H, ELAWAY R M, BARAKAT S, et al. Using Rough Set and Ant Colony Optimization in Feature Selection. International Journal of Emerging Trends and Technology in Computer Science, 2013, 2(1): 148-155.
[10] HU Y R, DING L X, XIE D T, et al. A Novel Discrete Artificial Bee Colony Algorithm for Rough Set-Based Feature Selection. International Journal of Advancements in Computing Technology, 2012, 4(6): 295-305.
[11] 李敬明,倪志伟,许莹,等.基于二进制萤火虫算法的属性选择方法研究.系统科学与数学, 2017, 37(2): 407-424.
(LI J M, NI Z W, XU Y, et al. Research on Attribute Selection Method Based on Binary Glowworm Swarm Optimization Algorithm. Journal of Systems Science and Mathematical Sciences, 2017, 37(2): 407-424.)
[12] 陆玉佳,倪志伟,朱旭辉,等.基于MapReduce改进离散型萤火虫算法和多重分形的属性约简方法.模式识别与人工智能, 2018, 31(6): 537-547.
(LU Y J, NI Z W, ZHU X H, et al. Attribute Reduction Method Based on MapReduce-Based Improved Discrete Glowworm Swarm Algorithm and Multi-fractal Dimension. Pattern Recognition and Artificial Intelligence, 2018, 31(6): 537-547.)
[13] 朱旭辉,倪志伟,倪丽萍,等.融合改进二元萤火虫算法和互补性测度的集成剪枝方法.电子与信息学报, 2018, 40(7): 1643-1651.
(ZHU X H, NI Z W, NI L P, et al. Improved Binary Glowworm Swarm Optimization Combined with Complementarity Measure for Ensemble Pruning. Journal of Electronics and Information Techno-logy, 2018, 40(7): 1643-1651.)
[14] 胡清华,赵辉,于达仁.基于邻域粗糙集的符号与数值属性快速约简算法.模式识别与人工智能, 2008, 21(6): 732-738.
(HU Q H, ZHAO H, YU D R. Efficient Symbolic and Numerical Attribute Reduction with Neighborhood Rough Sets. Pattern Recognition and Artificial Intelligence, 2008, 21(6): 732-738.)
[15] KRISHNANAND K N, GHOSE D. Glowworm Swarm Based Optimization Algorithm for Multimodal Functions with Collective Robo-tics Applications. Multiagent Grid Systems, 2006, 2: 209-222.
[16] LI M W, WANG X, GONG Y, et al. Binary Glowworm Swarm Optimization for Unit Commitment. Journal of Modern Power Systems and Clean Energy, 2014, 2(4): 357－365.
[17] 魏伟一,文雅宏.一种精英反向学习的萤火虫优化算法.智能系统学报, 2017, 12(5): 710-716.
(WEI W Y, WEN Y H. Firefly Optimization Algorithm Utilizing Elite Opposition-Based Learning. CAAI Transactions on Intelligent Systems, 2017, 12(5): 710-716.)
[18] 翟军昌,秦玉平.反向学习全局和声搜索算法.控制与决策, 2019, 34(7): 1450-1455.
(ZHAI J C, QIN Y P. Reverse Learning Global Harmony Search Algorithm. Control and Decision, 2019, 34(7): 1450-1455.)
[19] KOENKER R, MACHADO J A F. Goodness of Fit and Related Inference Processes for Quantile Regression. Journal of the American Statistical Association, 1999, 94(448): 1296-1310.
[20] 赵玉新,杨新社,刘利强.新兴元启发式优化方法.北京: 科学出版社, 2013.
(ZHAO Y X, YANG X S, LIU L Q. Emerging Meta-Heuristic Optimization Methods. Beijing, China: Science Press, 2013.)
[21] 程美英,倪志伟,朱旭辉.萤火虫优化算法理论研究综述.计算机科学, 2015, 42(4): 19-24.
(CHENG M Y, NI Z W, ZHU X H. Overview on Glowworm Swarm Optimization or Firefly Algorithm. Computer Science, 2015, 42(4): 19-24.)
[22] 刘长平,叶春明.具有Lévy飞行特征的蝙蝠算法.智能系统学报, 2013, 8(3): 240-246.
(LIU C P, YE C M. Bat Algorithm with the Characteristics of Lévy Flights. CAAI Transactions on Intelligent Systems, 2013, 8(3): 240-246.)
[23] MANTEGNA R N. Fast Accurate Algorithm for Numerical Simulation of Lévy Stable Stochastic Process. Physical Review E, 1992, 49(5): 4677-4653.
[24] 杨艳,周永权,罗林,等.人工萤火虫群优化算法求解约束优化问题.小型微型计算机系统, 2014, 35(1): 185-188.
(YANG Y, ZHOU Y Q, LUO L, et al. Artificial Glowworm Swarm Optimization Algorithm for Solving Constrained Optimization Problems. Journal of Chinese Computer Systems, 2014, 35(1): 185-188.)
[25] CHEN Y M, ZHU Q X, XU H R. Finding Rough Set Reducts with Fish Swarm Algorithm. Knowledge-Based Systems, 2015, 81: 22-29.
[26] LUAN X Y, LI Z P, LIU T Z. A Novel Attribute Reduction Algorithm Based on Rough Set and Improved Artificial Fish Swarm Algorithm.Neurocomputing, 2016, 174: 522-529.