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Boosting-based one-class SVM for recognizing true-fake Chinese liquors using electronic noses

机译：基于Boosting的一类SVM使用电子鼻识别真假白酒

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The true-fake detection of Chinese liquors can be considered as a one-class classification problem. Using only positive sample in training process brings about difficulties in determining the optimal parameters of one-class SVM and its kernel. And one-class SVM optimized with conventional grid search method has a low recognition rate of positive test samples. Hence, we proposed a new idea for recognition, namely boosting-based one-class SVM. Six different kinds of liquors were sampled by a self-designed electronic nose system. After the preprocessing and feature extraction of sample data, 18 groups of optimum parameters of one-class SVM were chosen from specified parameter range, each group was used to train a classifier with training algorithm. Then the true-fake decisions were made for test samples with boosting integration rules. Finally the recognition rates of Hongjinjiu and Lanjinjiu positive sample reached 95.24% and 100%, and the recognition rates of both positive and negative sample reached 97.04% and 94.83%, respectively.
机译：中国白酒的真伪检测可被视为一类分类问题。在训练过程中仅使用正样本会给确定一类SVM及其内核的最佳参数带来困难。传统的网格搜索方法优化的一类支持向量机对阳性测试样本的识别率较低。因此，我们提出了一种新的识别方法，即基于Boosting的一类SVM。通过自行设计的电子鼻系统对六种不同的酒进行了采样。在对样本数据进行预处理和特征提取后，从指定的参数范围中选择了18组一类SVM的最优参数，每组用于通过训练算法训练分类器。然后，使用增强的集成规则为测试样本做出真实的决策。最终，红金酒和兰金酒阳性样品的识别率分别达到95.24％和100％，阳性和阴性样品的识别率分别达到97.04％和94.83％。

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