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- W3184304730 abstract "This paper develops an approach to Manuka honey quality classification, using feature reduction and support vector machines (SVMs). We have developed a database of hyperspectral images of honey, including different quality Manuka honey. This paper focusses on machine learning techniques, specifically SVMs, to classify the quality of Manuka honey. We discuss the different types of multi-class SVMs and show the advantages of their different hierarchical structures. We look to evaluate the often overlooked tree-based multi-class SVM structures as they could have some advantages for our type of dataset. We also propose an ensemble technique for classifying the multi-class hyperspectral honey dataset, mitigating a key drawback of these tree-based methods, that they cannot correct for misclassifications. The results show that in our Manuka honey dataset, the SVMs with hierarchical structures can outperform the parallel based SVM structures and had the best performance overall when used in combination with the class embodiment autoencoder. The new ensemble technique is outperforming the benchmark SVMs for all the evaluated feature reduction techniques and is solving a drawback of the tree-based SVMs." @default.
- W3184304730 created "2021-08-02" @default.
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- W3184304730 date "2021-11-01" @default.
- W3184304730 modified "2023-09-30" @default.
- W3184304730 title "Developing a new ensemble approach with multi-class SVMs for Manuka honey quality classification" @default.
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- W3184304730 doi "https://doi.org/10.1016/j.asoc.2021.107710" @default.
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