模糊双超球学习机
|
摘要
受双支持向量机启发,提出模糊双超球学习机FTHLM。该方法试图为每类样本构造一个超球模型,通过构造一对超球模型将两类分类。模糊隶属度函数的引入有效地降低了奇异点和噪声点对分类结果的影响,从而保证FTHLM具有较高的分类效率。在UCI标准数据集上与支持向量机、双支持向量机的比较实验表明,所提FTHLM具有更优的分类能力。
Inspired by Twin Support Vector Machine( TWSVM), a Fuzzy Twin-Hypersphere Learning Machine( FTHLM) is proposed in this paper. It builds a hypersphere model for each type of sample,and uses a pair of hyperspheres to describe the samples in two classes,respectively. The membership function is introduced to decrease the influences of noise and singular points on classification results,so as to ensure that FTHLM has higher classification efficiency. Comparative experiments on the UCI datasets shows that FTHLM has better classification ability than Support Vector Machine( SVM) and TWSVM.
Inspired by Twin Support Vector Machine( TWSVM), a Fuzzy Twin-Hypersphere Learning Machine( FTHLM) is proposed in this paper. It builds a hypersphere model for each type of sample,and uses a pair of hyperspheres to describe the samples in two classes,respectively. The membership function is introduced to decrease the influences of noise and singular points on classification results,so as to ensure that FTHLM has higher classification efficiency. Comparative experiments on the UCI datasets shows that FTHLM has better classification ability than Support Vector Machine( SVM) and TWSVM.
引文
[1]SUN W W,LIU C,XU Y,et al.A band-weighted support vector machine method for hyperspectral imagery classification[J].IEEE Geoscience and Remote Sensing Letters,2017,14(10):1710-1714.
[2]WANG J,YANG D W,JIANG W,et al.Semi-supervised incremental support vector machine Learning based on neighborhood kernel estimation[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(10):2677-2687.
[3]FAN B,LU X J,LI H X.Probabilistic inference-based least squares support vector machine for modeling under noisy environment[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2016,46(12):1703-1710.
[4]ZHANG S L,SUI Y,ZHAO S C,et al.Graph-regularized structured support vector machine[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(6):1249-1262.
[5]王继东,宋智林,冉冉.基于改进支持向量机算法的光伏发电短期功率滚动预测[J].电力系统及其自动化学报,2016,28(11):9-13.
[6]王琴,沈远彤.基于压缩感知的多尺度最小二乘支持向量机[J].自动化学报,2016,42(4):631-640.
[7]张凯军,梁循.一种改进的显性多核支持向量机[J].自动化学报,2014,40(10):2288-2294.
[8]李凯,卢霄霞.一种基于粗糙间隔的模糊支持向量机[J].电子学报,2013,41(6):1183-1187.
[9]XU Y T,YANG Z J,PAN X L.A novel twinsupport vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
[10]丁世飞,张健,张谢锴,等.多分类孪生支持向量机研究进展[J].软件学报,2018,29(1):89-108.
[11]CHEN X B,YANG J,YE Q L,et al.Recursive projection twin support vector machine via within-class variance minimization[J].Pattern Recognition,2011,44(10):2643-2655.
[12]陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416.
[2]WANG J,YANG D W,JIANG W,et al.Semi-supervised incremental support vector machine Learning based on neighborhood kernel estimation[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(10):2677-2687.
[3]FAN B,LU X J,LI H X.Probabilistic inference-based least squares support vector machine for modeling under noisy environment[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2016,46(12):1703-1710.
[4]ZHANG S L,SUI Y,ZHAO S C,et al.Graph-regularized structured support vector machine[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(6):1249-1262.
[5]王继东,宋智林,冉冉.基于改进支持向量机算法的光伏发电短期功率滚动预测[J].电力系统及其自动化学报,2016,28(11):9-13.
[6]王琴,沈远彤.基于压缩感知的多尺度最小二乘支持向量机[J].自动化学报,2016,42(4):631-640.
[7]张凯军,梁循.一种改进的显性多核支持向量机[J].自动化学报,2014,40(10):2288-2294.
[8]李凯,卢霄霞.一种基于粗糙间隔的模糊支持向量机[J].电子学报,2013,41(6):1183-1187.
[9]XU Y T,YANG Z J,PAN X L.A novel twinsupport vector machine with pinball loss[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):359-370.
[10]丁世飞,张健,张谢锴,等.多分类孪生支持向量机研究进展[J].软件学报,2018,29(1):89-108.
[11]CHEN X B,YANG J,YE Q L,et al.Recursive projection twin support vector machine via within-class variance minimization[J].Pattern Recognition,2011,44(10):2643-2655.
[12]陈素根,吴小俊.改进的投影孪生支持向量机[J].电子学报,2017,45(2):408-416.