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C-Regularization Support Vector Machine for Seed Geometric Features Evaluation


Affiliations
1 School of Computer Science, Hunan University of Arts and Science, Changde, 415000, China
2 College of Computer Science, Beijing University of Technology, Beijing, 100022, China
 

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.

Keywords

C-Regularization, Misleading SV, Intelligent Evaluation, Unbalance, SVM.
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  • C-Regularization Support Vector Machine for Seed Geometric Features Evaluation

Abstract Views: 159  |  PDF Views: 39

Authors

Xi Jinju
School of Computer Science, Hunan University of Arts and Science, Changde, 415000, China
Tan Wenxue
College of Computer Science, Beijing University of Technology, Beijing, 100022, China

Abstract


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.

Keywords


C-Regularization, Misleading SV, Intelligent Evaluation, Unbalance, SVM.

References