基于PSO-ELM特征映射的KNN分类算法
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摘要
针对传统极端学习机算法(ELM)和K近邻分类算法(KNN)在处理分类问题中存在的问题,提出一种基于PSOELM特征映射的KNN分类算法。该算法利用ELM的输入层权值和隐层神经元对输入样本进行非线性映射,并利用粒子群算法(PSO)寻找一组最优的ELM映射参数,再将映射后的特征样本输入到KNN算法中,提高处理线性不可分问题的能力。在多个数据集上的实验结果表明,文中算法比KNN改进算法以及ELM改进算法有更高的分类正确率。
The K-nearest neighbor(KNN)classification algorithm based on PSO-ELM(particle swarm optimization-extreme learning machine) feature mapping is proposed because the traditional ELM algorithm and KNN classification algorithm have some shortcomings in classification process. The input-layer weight and hidden-layer neuron of ELM algorithm are used to perform the nonlinear mapping for the input sample. The PSO algorithm is used to find a group of optimal ELM mapping parameters,and then the mapped feature sample is input into KNN algorithm,which can improve the ability to deal with the linear impartibility problem. The experimental results of several data sets show that the proposed algorithm has higher classification accuracy than improved KNN algorithm and improved ELM algorithm.
The K-nearest neighbor(KNN)classification algorithm based on PSO-ELM(particle swarm optimization-extreme learning machine) feature mapping is proposed because the traditional ELM algorithm and KNN classification algorithm have some shortcomings in classification process. The input-layer weight and hidden-layer neuron of ELM algorithm are used to perform the nonlinear mapping for the input sample. The PSO algorithm is used to find a group of optimal ELM mapping parameters,and then the mapped feature sample is input into KNN algorithm,which can improve the ability to deal with the linear impartibility problem. The experimental results of several data sets show that the proposed algorithm has higher classification accuracy than improved KNN algorithm and improved ELM algorithm.
引文
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[12]卢诚波,林银河,梅颖.基于ELM特征映射的KNN算法[J].复旦学报(自然科学版),2016,55(5):570-575.LU Chengbo,LIN Yinhe,MEI Ying. KNN based on extreme learning machine feature mapping[J]. Journal of Fudan University(natural science),2016,55(5):570-575.
[13] SARASWATHI S. ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented[J]. IEEE/ACM transactions on computational biology and bioinformatics,2011,8(2):452-463.
[14] UCI. UCI machine learning database[DB/OL].[2012-04-23].http://archive.ics.uci.edu/ml/datasets.html.
[2]周英,卓金武,卞月青.大数据挖掘[M].北京:机械工业出版社,2016.ZHOU Ying,ZHUO Jinwu,BIAN Yueqing. Big data mining[M]. Beijing:China Machine Press,2016.
[3] GUO G,WANG H,BELL D,et al. KNN model-based approach in classification[C]//2003 International Conference on the Move to Meaningful Internet Systems.[S. l.]:Springer,2003:986-996.
[4] HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing,2006,70(1/3):489-501.
[5] HUANG G B,ZHOU H,DING X,et al. Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems,man&cybernetics,2012,42(2):513-529.
[6]李彬,李贻斌.基于ELM学习算法的混沌时间序列预测[J].天津大学学报,2011,44(8):701-704.LI Bin,LI Yibin. Chaotic time series prediction based on ELM learning algorithm[J]. Journal of Tianjin University,2011,44(8):701-704.
[7] MARTíNEZ J M,ESCANDELL-MONTERO P,SORIA OLIVAS E,et al. Regularized extreme leaning machine for regression problems[J]. Neurocomputing,2011,74(17):3716-3721.
[8] YU K,LIANG J,ZHANG X. Kernel nearest neighbor algorithm[J]. Neural processing letters,2002,15(2):147-156.
[9] SHAWE-TAYLOR J,CRISTIANINI N. Kernel method for pattern analysis[M]. New York:Cambridge University Press,2004.
[10] CAMASTRA F,VERRI A. A novel kernel method for clustering[J]. IEEE transactions on pattern analysis and machine intelligence,2005,27(5):245-250.
[11] ALSHAMIRI A K,SURAMPUDI B R,SINGH A. A novel ELM K-Means algorithm for clustering[C]//2014 International Conference on Swarm,Evolutionary,and Memetic Computing. Switzerland:Springer,2015:212-222.
[12]卢诚波,林银河,梅颖.基于ELM特征映射的KNN算法[J].复旦学报(自然科学版),2016,55(5):570-575.LU Chengbo,LIN Yinhe,MEI Ying. KNN based on extreme learning machine feature mapping[J]. Journal of Fudan University(natural science),2016,55(5):570-575.
[13] SARASWATHI S. ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented[J]. IEEE/ACM transactions on computational biology and bioinformatics,2011,8(2):452-463.
[14] UCI. UCI machine learning database[DB/OL].[2012-04-23].http://archive.ics.uci.edu/ml/datasets.html.