计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 49-53.doi: 10.11896/j.issn.1002-137X.2016.03.009
张谢锴,丁世飞
ZHANG Xie-kai and DING Shi-fei
摘要: 孪生支持向量机(TWSVM)的研究是近来机器学习领域的一个热点。TWSVM具有分类精度高、训练速度快等优点,但训练时没有充分利用样本的统计信息。作为TWSVM的改进算法,基于马氏距离的孪生支持向量机(TMSVM)在分类过程中考虑了各类样本的协方差信息,在许多实际问题中有着很好的应用效果。然而TMSVM的训练速度有待提高,并且仅适用于二分类问题。针对这两个问题,将最小二乘思想引入TMSVM,用等式约束取代TMSVM中的不等式约束,将二次规划问题的求解简化为求解两个线性方程组,得到基于马氏距离的最小二乘孪生支持向量机(LSTMSVM),并结合有向无环图策略(DAG)设计出基于马氏距离的最小二乘孪生多分类支持向量机。为了减少DAG结构的误差累积,构造了基于马氏距离的类间可分性度量。人工数据集和UCI数据集上的实验均表明,所提算法不仅有效,而且相对于传统多分类SVM,其分类性能有明显提高。
[1] Cortes C,Vapnik V N.Support vector networks[J].MachinesLearning,1995,20(2):273-297 [2] Vapnik V N.The nature of statistical learning theory[M].New York:Springer,1998 [3] Mangasarian O L,Wild E.Multisurface proximal support vector machine classification via generalized eigenvalues[J].IEEE Transactions on Pattern Aalysis and Machine Intelligence,2006,28(1):69-74 [4] Jayadeva,Khemchandni R,Chandra S.Twin support vector machines for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910 [5] Ding Shie-fei,Yu Jun-zhao,Qi Bing-juan,et al.An overview on twin support vector machines[J].Artificial Intelligence Review,2014,42(2):245-252 [6] Kumar M A,Gopal M.Least squares twin support vector ma-chines for pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543 [7] Shao Yuan-hai,Chen Wei-jie,Zhang Jing-jing,et al.An efficient weighted Lagrangian twin support vector machine for imba-lanced data classification[J].Pattern Recognition,2014,47(9):3158-3167 [8] Wang Di,Ye Qiao-lin,Ye Ning.Localized multi-plane TWSVM classifier via manifold regularization[C]∥2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics,2011.New York:IEEE,2010:70-73 [9] Wang Ya-nan,Zhao Xi,Tian Ying-jie.Local and global regula-rized twin SVM[J].Procedia Computer Science,2013,18:1710-1719 [10] Kumar M A,Gopal M.Application of smoothing technique on twin support vector machines[J].Pattern Recognition Letters,2008,29(13):1842-1848 [11] Ding Shi-fei,Huang Hua-juan,Yu Jun-zhao,et al.Polynomial smooth twin support vector machines based on invasive weed optimization algorithm[J].Journal of Computers,2014,9(5):2063-2071 [12] Ding Shi-fei,Huang Hua-juan,Shi Zhong-zhi.Weighted smooth CHKS twin support vector machines[J].Journal of Software,2013,4(11):2548-2557(in Chinese) 丁世飞,黄华娟,史忠植.加权光滑CHKS孪生支持向量机[J].软件学报,2013,24(11):2548-2557 [13] Ding Shi-fei,Wu Fu-lin,Shi Zhog-zhi.Wavelet twin support vector machine[J].Neural Computing and Applications,2014,25(6):1241-1247 [14] Xie Xi-jiong,Sun Shi-liang.Multitask centroid twin support vector machines[J].Neurocomputing,2015,149(2):1085-1091 [15] Peng Xin-jun,Xu Dong.Twin Mahalanobis distance-based support vector machines for pattern recognition[J].Information Sciences,2012,200:22-37 [16] Chu Mao-xiang,Wang An-na,Gong Rong-fei,et al.Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects[J].Journal of Iron and Steel Research International,2014,21(2):174-180 [17] Xu Yi-tian,Guo Rui.A twin hyper-sphere multi-class classification support vector machine[J].Journal of Intelligent and Fuzzy Systems,2014,27(4):1783-1790 [18] Yang Zhi-xia,Shao Yuan-hai,Zhang Xiang-sun.Multiple birth support vector machine for multi-class classification[J].Neural Computing and Applications,2013,22(1):153-161 [19] Chen J,Ji G R.Multi-class LSTSVM classifier based on optimal directed acyclic graph[C]∥The 2nd International Conference on Computer and Automation Engineering,2010.New York:IEEE,2010:100-104 [20] Xu Yi-tian,Guo Rui,Wang Lai-sheng.A twin multi-class classification support vector machine[J].Cognitive Computation,2013,5(4):580-588 [21] Xie Jun-ying,Hone Ka-ta,Xie Wei-xin,et al.Extending twinsupport vector machine classifier for multi-category classification problems[J].Intelligent Data Analysis,2013,17(4):649-664 [22] Shao Yuan-hai,Chen Wei-jie,Huang Wen-biao,et al.The best separating decision tree twin support vector machine for multi-class classification[J].Procedia Computer Science,2013,17:1032-1038 [23] Wang Xue-min.Applied multivariate analysis(4th edition)[M].Shanghai:Shanghai University of Finance and Economics Press,2014:25-34(in Chinese) 王学民.应用多元分析(第4版)[M].上海:上海财经大学出版社,2014:25-34 |
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