计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 93-101.doi: 10.11896/jsjkx.210100067
张亚钏1, 李浩1, 宋晨明2, 卜荣景1, 王海宁1, 康雁1
ZHANG Ya-chuan1, LI Hao1, SONG Chen-ming2, BU Rong-jing1, WANG Hai-ning1, KANG Yan1
摘要： 包装器特征选择是一种数据预处理方法,通过筛选出信息量最大的特征来降低原始数据集的维数,同时使分类特征的精度最大化。为提高包装器特征选择能力,提出了一种混合人工化学反应狼群优化算法——ACR-WCA。ACR-WCA算法采用自然策略,模仿狼群的搜索策略,可以快速向解空间靠拢,再采用人工化学反应策略优化狼群的种群行为,快速找到最优解,解决局部最优问题;其次,为有效处理数据特征,在初始化阶段利用转换函数处理成二进制特征问题;之后,结合分类准确率和特征选择数给出算法的适应度函数。同时,采用k最近邻(KNN)分类器对测试数据进行训练,并通过K-折交叉验证来克服过拟合问题。实验基于21个著名的不同维度数据集训练,并与4种传统方法和3种接近方法进行比较。实验结果表明,该算法是高效可靠的,它可以对大量特征进行分类任务,具有较高的准确率。
中图分类号:
| [1]ZHONG X,MA Z P,ZHANG B.Overview of Data Mining[J].Pattern Recognition and Artificial Intelligence,2001(1):50-57. [2]HAN J W,KAMBER M.Data mining concepts and techniques[M].Beijing:China Machine Press,2012. [3]WU H S,ZHANG F M,WU L S.New swarm intelligence algorithm-wolf pack algorithm[J].Systems Engineering and Electronics,2013(11):204-212. [4]KENNEDY J,EBERHART R C.Particle swarm optimization[C]//Proc IEEE International Conference on Neural Networks.IV,Perth,Australia,1995:1942-1948. [5]HOLLAND J H.Adaptation in natural and artificial system[M].Ann Ar-bor:The University of Michigan Press,1975:68-73. [6]LAM A Y S,LI V O K.Cheical-Reaction-Inspired Metaheuristic for Optimization[J].IEEE Transactions on Evolutionary Computation,2010,14(3):381-399. [7]KOZODOI N,LESSMANN S,PAPAKONSTANTINOU K,et al.A multi- objective approach for profit-driven feature selection in credit scoring[J].Decision Support Systems,2019,120:106-117. [8]HE X,CAI D,NIYOGI P.Laplacian score for feature selection[C]//Advances in Neural Information Processing Systems.2006:507-514. [9]ARAUZO-AZOFRA A,BENITEZ J M,CASTRO J L.A feature set measure based on relief[C]//Proceedings of the fifth international conference on Recent Advances in Soft Computing.2004:104-109. [10]SHI W F,HU X G,YU K.K-part Lasso based on feature selection algorithm for high-dimensional data[J].Computer Engineeringand Application,2012,48(1):157-161. [11]WANG Q T,Application of meta heuristic algorithm in discrete location[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2010. [12]BEYER H G,SCHWEFEL H P.Evolution strategies- A comprehensive introduction[J].Natural Computing,2002,1(1):3-52. [13]KOZA J R.Genetic programming as a means for programming computers by natural selection[J].Statistics and Computing,1994,4(2):87-112. [14]DORIGO M,BIRATTARI M,STÜTZLE T.Ant Colony Optimization[J].IEEE Computational Intelligence Magazine,2006,1(4):28-39. [15]YANG X S.Firefly Algorithm,Stochastic Test Functions andDesign Optimisation[J].International Journal of Bio Inspired Computation,2010,2(2):78-84. [16]KIRKPATRICK S,GELATT C D,VECCHI M P.Optimization by simulated annealing[J].Science,1983,220(4598):671-680. [17]RASHEDI E,NEZAMABADI-POUR H,SARYAZDIS.GSA:a Gravitational Search Algorithm[J].Information Sciences,2009,179(13):2232-2248. [18]GEEM Z W,KIM J H,LOGANATHANG V.A New Heuristic Optimization Algorithm:Harmony Search[J].Simulation,2001,2(2):60-68. [19]KAVEH A,KHAYATAZADM.A new meta-heuristic method:Ray Optimization[J].Computers & Structures,2012,112-113(DEC.):283-294. [20]ATASHPAZ-GARGARI E,LUCAS C.Imperialist competitivealgorithm:An algorithm for optimization inspired by imperialistic competition[C]//IEEE Congress on Evolutionary Computation.IEEE,2008. [21]KASHAN A H.League Championship Algorithm:A New Algorithm for Numerical Function Optimization[C]//International Conference of Soft Computing and Pattern Recognition.Malacca,2009:43-48. [22]GHORBANI N,BABAEI E.Exchange market algorithm[J].Applied Soft Computing Journal,2014,19:177-187. [23]ALATAS B.ACROA:Artificial Chemical Reaction Optimization Algorithm for global optimization[J].Expert Systems with Applications,2011,38(10):13170-13180. [24]BECHIKH S,CHAABANI A,BEN SAID L.An EfficientChemical Reaction Optimization Algorithm for Multiobjective Optimization[J].IEEE Trans. Cybern,2015,45(10):2051-2064. [25]TRUONG T K,LI K,XU Y.Chemical reaction optimizationwith greedy strategy for the 0-1 knapsack problem[M].Elsevier Science Publishers B.V.,2013. [26]MORADI P,GHOLAMPOUR M.A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy[J].Applied Soft Computing,2016,43(C):117-130. [27]YU H,ZHAO N,WANG P,et al.Chaos-enhanced synchronized bat optimizer[J].Applied Mathematical Modelling,2019,77. [28]MAFARJA M M,MIRJALILI S.Hybrid Whale OptimizationAlgorithm with simulated annealing for feature selection[J].Neurocomputing,2017,260:302-312. [29]KENNEDY J,EBERHART R C.Particle Swarm Optimization[C]//Procedings of IEEE Conference on Neural Networks.Perth:IEEE,1995:1942-1948. [30]YANG X S.Flower Pollination Algorithm for Global Optimiza-tion[C]//International Conference on Unconventional Compu- ting and Natural Computation.Berlin:Springer,2012. [31]MIRJALILI S,MIRJALILI S M,YANG X S.Binary bat algorithm[J].Neural Computing & Applications,2014,25(3):663-618. [32]MIRJALILI S,MIRJALILI S M,HATAMLOU A.Multi-Verse Optimizer:a nature-inspired algorithm for global optimization[J].Neural Computing and Applications,2015,27(2):495-513. [33]SOUZA R C T D,COELHO L D S,MACEDO C A D,et al.A V-Shaped Binary Crow Search Algorithm for Feature Selection[C]//2018 IEEE Congress on Evolutionary Computation(CEC).IEEE,2018. [34]HUSSIEN A G,HASSANIEN A E,HOUSSEIN E H,et al.S-shaped Binary Whale Optimization Algorithm for Feature Selection[M]//Recent Trends in Signal and Image Processing.Berlin:Springer,2019:79-87. [35]ABDEL-BASSET M,EL-SHAHAT D,EL-HENAWY I,et al.A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection[J].Expert Systems with Applications,2020,139:112824. |
| [1] | 鲁晨阳, 邓苏, 马武彬, 吴亚辉, 周浩浩. 基于分层抽样优化的面向异构客户端的联邦学习 Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients 计算机科学, 2022, 49(9): 183-193. https://doi.org/10.11896/jsjkx.220500263 |
| [2] | 陈志强, 韩萌, 李慕航, 武红鑫, 张喜龙. 数据流概念漂移处理方法研究综述 Survey of Concept Drift Handling Methods in Data Streams 计算机科学, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112 |
| [3] | 周旭, 钱胜胜, 李章明, 方全, 徐常胜. 基于对偶变分多模态注意力网络的不完备社会事件分类方法 Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification 计算机科学, 2022, 49(9): 132-138. https://doi.org/10.11896/jsjkx.220600022 |
| [4] | 武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航. 监督和半监督学习下的多标签分类综述 Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning 计算机科学, 2022, 49(8): 12-25. https://doi.org/10.11896/jsjkx.210700111 |
| [5] | 李其烨, 邢红杰. 基于最大相关熵的KPCA异常检测方法 KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion 计算机科学, 2022, 49(8): 267-272. https://doi.org/10.11896/jsjkx.210700175 |
| [6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [7] | 李斌, 万源. 基于相似度矩阵学习和矩阵校正的无监督多视角特征选择 Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment 计算机科学, 2022, 49(8): 86-96. https://doi.org/10.11896/jsjkx.210700124 |
| [8] | 王灿, 刘永坚, 解庆, 马艳春. 基于软标签和样本权重优化的Anchor Free目标检测算法 Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization 计算机科学, 2022, 49(8): 157-164. https://doi.org/10.11896/jsjkx.210600240 |
| [9] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [10] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [11] | 陈俊, 何庆, 李守玉. 基于自适应反馈调节因子的阿基米德优化算法 Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor 计算机科学, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150 |
| [12] | 王兵, 吴洪亮, 牛新征. 基于改进势场法的机器人路径规划 Robot Path Planning Based on Improved Potential Field Method 计算机科学, 2022, 49(7): 196-203. https://doi.org/10.11896/jsjkx.210500020 |
| [13] | 唐枫, 冯翔, 虞慧群. 基于自适应知识迁移与资源分配的多任务协同优化算法 Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation 计算机科学, 2022, 49(7): 254-262. https://doi.org/10.11896/jsjkx.210600184 |
| [14] | 张翀宇, 陈彦明, 李炜. 边缘计算中面向数据流的实时任务调度算法 Task Offloading Online Algorithm for Data Stream Edge Computing 计算机科学, 2022, 49(7): 263-270. https://doi.org/10.11896/jsjkx.210300195 |
| [15] | 赵冬梅, 吴亚星, 张红斌. 基于IPSO-BiLSTM的网络安全态势预测 Network Security Situation Prediction Based on IPSO-BiLSTM 计算机科学, 2022, 49(7): 357-362. https://doi.org/10.11896/jsjkx.210900103 |
|
||
首页