Fuzzy c-means based support vector machines classifier for perfume recognition
Fuzzy c-means based support vector machines classifier for perfume recognition
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DOI：
10.1016/j.asoc.2016.05.030
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Engin Esme;B. Karlik
中科院分区：
文献类型：
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作者：
Engin Esme;B. Karlik
对于人的鼻子来说，识别三种以上的香水是非常困难的。使用具有多个传感器的电子鼻来识别香水气味模式也是一个问题。因此，提出了一种新的混合分类器，用于从 20 种不同香水气味的非常相似的数据集中识别香水类型。这种混合技术的结构是无监督模糊聚类c均值（FCM）和有监督支持向量机（SVM）的组合。另一方面，将这种提出的软计算技术与其他众所周知的学习算法进行了比较。结果表明，所提出的混合算法的准确率比其他算法提高了 97.5%。
Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.