Open Access Subscription Access
C-Regularization Support Vector Machine for Seed Geometric Features Evaluation
People have been utilizing Support Vector Machine (SVM) to tackle the problem of data mining and machine learning related to many practicalities. However, for some training set of multi-group which presents unbalance of the number of samples, a classifier model trained by C-SVM always results in some unbalanced error-rates. Grounded upon analysis of Lagrange multiplier, the paper proposes the Misleading-SV, outer boundary of class, learning-error-rate and other concepts, and formulates the C-Regularization SVM and a method for regularizing slack constant C. Taking aim at wheat seed geometric property evaluation for quality gradation, the project crew develops some test experiments for algorithm validation. The contour analysis reveals the proposed scheme can effectively grade wheat seeds by their geometric features with an precision rate of 96%. Especially against some prior algorithms, result of contrast experiment demonstrates that for the subject with sparse samples, the method for regularizing slack constant can lower the macro classification error-rate of classifier obviously.
C-Regularization, Misleading SV, Intelligent Evaluation, Unbalance, SVM.
- C. Cortes and V. Vapnik, "Support-vector networks," MACHINE LEARNING, vol. 20, no. 3, pp. 273-297, 1995.
- L. P. S. Z.-h. ZHENG En-hui, XU Hong, "Mining knowledge from unbalanced data based on-support vector machine," Journal of Zhejiang University(Engineering Science), vol. 40, no. 10, pp. 1682-1687, 2006.
- W. J. Zhou, G. Y. Jiang, and M. Yu, "An objective image quality assessment model based on block content and support vector regression," Gaojishu Tongxin/Chinese High Technology Letters, vol. 22, no. 11, pp. 1117-1123, 2012.
- Z. M. Yang, Y. J. Tian, and G. L. Liu, "Fuzzy support vector machine method for city air quality assessment," Journal of China Agricultural University, vol. 11, no. 5, pp. 92-97, 2006.
- Y. Y. YUAN XingMei, YANG Ming, "An ensemble classifier based on structural support vector machine for imbalanced data," PR & AI, vol. 26, no. 3, pp. 315-320, 2013.
- B. Scholkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, "New support vector algorithms," NEURAL COMPUTATION, vol. 12, no. 5, pp. 1207-1245, 2000.
- J. C. Wang, J. Hu, N. N. Liu, H. M. Xu, and S. Zhang, "Investigation of combining plant genotypic values and molecular marker information for constructing core subsets," JOURNAL OF INTEGRATIVE PLANT BIOLOGY, vol. 48, no. 11, pp. 1371-1378, 2006.
- C. J. Zhao, Y. S. Wang, J. H. Wang, X. Y. Hao, Y. Liu, and Z. K. Feng, "Study on key technologies of location-based service (lbs) for forest resource management," Sensor Letters, vol. 10, no. 1-2, pp. 292-300, 2012.
- T. T. Chang, H. W. Liu, and S. S. Zhou, "Large scale classification with local diversity adaboost svm algorithm," Journal of Systems Engineering and Electronics, vol. 20, no. 6, pp. 1344-1350, 2009.
- J. Li, Y. G. Zu, M. Luo, C. B. Gu, C. J. Zhao, T. Efferth, and Y. J. Fu, "Aqueous enzymatic process assisted by microwave extraction of oil from yellow horn (xanthoceras sorbifolia bunge.) seed kernels and its quality evaluation," Food Chemistry, vol. 138, no. 4, pp. 2152-2158, 2013.
- Y. Y. Wang and S. C. Chen, "Soft large margin clustering," Information Sciences, vol. 232, pp. 116-129, 2013.
- J. Wu, C. Deng, X. Shao, and K. Mao, "A novel equipment reliability estimation method based on svr," Gaojishu Tongxin/Chinese High Technology Letters, vol. 21, no. 10, pp. 1095-1100, 2011.
- S. S. Keerthi and C. J. Lin, "Asymptotic behaviors of support vector machines with gaussian kernel," NEURAL COMPUTATION, vol. 15, no. 7, pp. 1667-1689, 2003.
- H. J. Fan and Q. Song, "A linear recurrent kernel online learning algorithm with sparse updates," Neural Networks, vol. 50, pp. 142-153, 2014.
- Y. Willi, "The battle of the sexes over seed size: Support for both kinship genomic imprinting and interlocus contest evolution," American Naturalist, vol. 181, no. 6, pp. 787- 798, 2013.
- N. Segata and E. Blanzieri, "Fast and scalable local kernel machines," Journal of Machine Learning Research, vol. 11, pp. 1883-1926, 2010.
- M. R. Daliri, "Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis," Biomedizinische Technik, vol. 57, no. 5, pp. 395-402, 2012.
- G. Z. Qiang, H. Q. Wang, and L. Quan, "Financial time series forecasting using lpp and svm optimized by pso," Soft Computing, vol. 17, no. 5, pp. 805-818, 2013.
- J. C. Alvarez Anton, P. J. Garcia Nieto, F. J. de Cos Juez, F. Sanchez Lasheras, M. Gonzalez Vega, and M. N. Roqueni Gutierrez, "Battery state-of-charge estimator using the svm technique," APPLIED MATHEMATICAL MODELLING, vol. 37, no. 9, pp. 6244-6253, 2013.
- C. Tan, T. Wu, and X. Qin, "Multi-class support vector machine for on-line spectral quality monitoring of tobacco products," ASIAN JOURNAL OF CHEMISTRY, vol. 25, no. 7, A, pp. 3668-3672, 2013.
- R. Du, Q. Wu, X. J. He, and J. Yang, "Mil-skde: Multiple instance learning with supervised kernel density estimation," Signal Processing, vol. 93, no. 6, pp. 1471-1484, 2013.
- X. J. Ding, Y. L. Zhao, and Y. C. Li, "Secondary descent active set algorithm based on svm," Tien Tzu Hsueh Pao/Acta Electronica Sinica, vol. 39, no. 8, pp. 1766-1770, 2011.
- R. Stoean and C. Stoean, "Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection," Expert Systems with Applications, vol. 40, no. 7, pp. 2677-2686, 2013.
- G. Caruana, M. Z. Li, and Y. Liu, "An ontology enhanced parallel svm for scalable spam filter training," Neurocomputing, vol. 108, pp. 45-57, 2013.
Abstract Views: 153
PDF Views: 38