Recognition of the hardness of licorice seeds using a semi-supervised learning method and near-infrared spectral data

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• 作者：Liming Yang^a ; ^{cauylm@126.com} ; Qun Sun^b ; ¹ ; ^{sqcau@126.com}
• 关键词：Licorice seeds classification ; Near-infrared spectroscopy ; Support vector machine ; Semi-supervised support vector machine ; DC programming
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2012
• 期刊代码：21_01697439
• 类别：ch
• 出版时间：15 May, 2012
• 卷：114
• 期：Complete
• 页码：109-115
• 文件大小：448 K

摘要

The recognition of the hardness of licorice seeds is a challenging task. The purpose of this investigation is to identify the hardness of licorice seeds employing a semi-supervised learning method and near-infrared spectroscopy. An excellent semi-supervised learning model, the semi-supervised support vector machine (S³VM), is built using the small labeled samples and the large unlabeled samples. Moreover, the proposed model is solved by employing an effective method, the robust DC (difference of convex functions) programming. The resulting algorithm only requires the solving of a few linear programs. Furthermore, this model is used for the direct classification of licorice samples. Comparing with the supervised support vector machine (SVM), experimental results on different spectral regions show that incorporating unlabeled samples in training improves the generalization when insufficient training information is available. Moreover, our method outperforms the existing S³VM method by obtaining better performance in different spectral regions. These results show that it is possible to identify the hardness of licorice seeds using the proposed S³VM and near-infrared spectroscopic data. We hope that the results obtained in this study will help further investigations of the hardness of crop seeds.