Improvements on twin-hypersphere support vector machine using local density information

被引：9
作者：
Ai Q. [1 ,2 ]
Wang A. [1 ]
Wang Y. [1 ]
Sun H. [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning
[2] School of Software, University of Science and Technology Liaoning, Anshan, 114051, Liaoning
来源：
Progress in Artificial Intelligence | 2018年 / 7卷 / 3期
关键词：
Local density; Pattern recognition; Pruning dataset; Twin-hypersphere support vector machine;
D O I：
10.1007/s13748-018-0141-0
中图分类号：
学科分类号：
摘要：
In this paper, we propose a novel binary classifier called twin-hypersphere support vector machine with local density information (LDTHSVM). Firstly we extract local density for each training sample and treat it as the weight of that sample, next prune training dataset according to these local density degrees, finally introduce these local density degrees into twin-hypersphere support vector machine (THSVM) and reconstruct classification model on the pruned training dataset. LDTHSVM not only inherits good properties from THSVM, but also gives more robust description for dataset. The experimental results on synthetic and publicly available benchmark datasets show the excellent performance of the LDTHSVM classifier in terms of classification accuracy and learning time. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码：167 / 175
页数：8
相关论文
共 35 条
  • [1] Ar J., Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1, pp. 1-30, (2006)
  • [2] Chen W.J., Shao Y.H., Li C.N., Deng N.Y., MLTSVM: a novel twin support vector machine to multi-label learning, Pattern Recogn., 52, pp. 61-74, (2015)
  • [3] Fan R.E., Chen P.H., Lin C.J., Working set selection using second order information for training support vector machines, J. Mach. Learn. Res., 6, 4, pp. 1889-1918, (2005)
  • [4] Garcia S., Fernandez A., Luengo J., Herrera F., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power, Inf. Sci., 180, 10, pp. 2044-2064, (2010)
  • [5] Khemchandani R.J., Chandra R.S., Twin support vector machines for pattern classification, IEEE Trans. Pattern Anal. Mach. Intell., 29, 5, pp. 905-910, (2007)
  • [6] Joachims T., Making large-scale support vector machine learning practical, Advances in Kernel Methods-Support Vector Learning, pp. 169-184, (1999)
  • [7] Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.R.K., A fast iterative nearest point algorithm for support vector machine classifier design, IEEE Trans. Neural Netw., 11, 1, pp. 124-136, (2000)
  • [8] Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.R.K., Improvements to platt’s SMO algorithm for SVM classifier design, Neural Comput., 13, 3, pp. 637-649, (2001)
  • [9] Kumar M.A., Gopal M., Application of smoothing technique on twin support vector machines, Pattern Recogn. Lett., 29, 13, pp. 1842-1848, (2008)
  • [10] Kumar M.A., Gopal M., Least squares twin support vector machines for pattern classification, Expert Syst. Appl., 36, 4, pp. 7535-7543, (2009)
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