Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (3): 8-14.doi: 10.6040/j.issn.1672-3961.0.2017.417

• Machine Learning & Data Mining • Previous Articles     Next Articles

Support vector regression algorithm based on kernel similarity reduced strategy

Yingda LI(),Zongxia XIE   

  1. School of Computer Software, Tianjin University, Tianjin 300350, China
  • Received:2017-08-24 Online:2019-06-20 Published:2019-06-27
  • Supported by:
    国家基金面向大数据的粒计算理论与方法资助项目(61432011)

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Abstract:

With the increase of iterations, SGD was susceptible to the curse of kernelization. We introduced the kernel similarity to remove the redundant SVs called SVs-reduced strategy (SRS) for improving the efficiency of SGD in large scale non-linear modeling. At each iteration in SGD, the similarities between a new sample and the recorded SVs were computed if the new sample was a SV. If the similarity was larger than a threshold, this SV was not saved. Experimental results on UCI, LIBSVM and Wind-speed datasets demonstrated that the proposed SRS could be used to solve large-scale non-linear SVR comparing with some state-of-the-art algorithms.

Key words: support vector regression, efficient algorithms, stochastic gradient descent, kernel similarity

CLC Number: 

  • TP181

Table 1

Properties of different datasets"

编号 数据集 实例数 训练样本数 测试样本数 属性数
1 Cadata 20 640 180 000 2 640 8
2 CASP 45 730 40 000 5 730 9
3 Cpusmall 8 192 600 000 2 192 12
4 Space_ga 3 107 250 000 607 6
5 TFIDF-2006 16 087 16 087 3 308 150 360
6 Wind-ningxia 51 840 45 000 6 840 24
7 Wind-farm 246 360 246 360 26 360 6

Table 2

MSE and SVs of different algorithms"

数据集 SVR_SRS SVR_BSGD SVR_SGD LIBSVM
Kreduce MSE #SVs MSE #SVs MSE #SVs MSE #SVs
Cadata 0.99 0.014 4 6 237 0.014 0 500 0.013 8 61 692 0.014 0 51 871
CASP 0.98 0.044 2 19 873 0.049 1 500 0.046 2 25 658 0.044 4 21 997
Cpusmall 0.99 0.002 1 836 0.002 2 500 0.002 2 5 938 0.002 3 4 314
Space_ga 0.99 0.002 2 149 0.002 3 500 0.002 3 4 749 0.001 8 3 685
TFIDF-2006 0.98 0.146 4 765 0.153 2 500 0.140 3 1 878 0.140 3 11 410
Wind-ningxia 0.97 0.490 1 10 741 0.614 6 500 0.510 2 23 154 0.470 4 35 469
Wind-farm 0.99 4.118 9 4 249 4.248 5 500 4.2 21 2 183 413 3.936 4 232 965

Table 3

Training time comparison of algorithms"

数据集 SVR_SRS SVR_BSGD SVR_SGD LIBSVM
Cadata 41.87 146.15 417.11 817.01
CASP 29.67 62.53 47.77 101.24
Cpusmall 35.53 43.58 109.11 334.76
Space_ga 5.67 17.31 24.88 33.28
TFIDF-2006 1 021.26 1 411.99 1 658.66 1 611.85
Wind-ningxia 44.95 54.42 167.73 1 258.58
Wind-farm 44.81 540.45 1 722.33 2 452.69

Fig.1

Comparison of MSE and training time evolution curves on Cadata dataset"

Fig.2

Comparison of MSE and training time evolution curves on CASP dataset"

Fig.3

Comparison of MSE and training time evolution curves on Wind-Ningxia dataset"

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