Classification of Camellia species from 3 sections using leaf anatomical data with back-propagation neural networks and support vector machines

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buir.contributor.authorÖzaktaş Barshan, Billur
buir.contributor.authorMantri, Nitin
dc.citation.epage1103en_US
dc.citation.issueNumber6en_US
dc.citation.spage1093en_US
dc.citation.volumeNumber37en_US
dc.contributor.authorJiang, W.en_US
dc.contributor.authorÖzaktaş Barshan, Billuren_US
dc.contributor.authorMantri, Nitinen_US
dc.contributor.authorTao, Z.en_US
dc.contributor.authorLu, H.en_US
dc.date.accessioned2016-02-08T09:34:21Z
dc.date.available2016-02-08T09:34:21Z
dc.date.issued2013en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractLeaf characteristics provide many useful clues for taxonomy. We used a back-propagation artificial neural network (BPANN) and C-support vector machines (C-SVMs) to classify 47 species from 3 sections of genus Camellia (16 from sect. Chrysanthae, 16 from sect. Tuberculata, and 15 from sect. Paracamellia). The classification model was constructed based on 7 leaf anatomy attributes including, area of adaxial epidermal cell, thickness of adaxial epidermal cell, thickness of palisade parenchyma, thickness of total leaf, thickness of spongy parenchyma, thickness of abaxial epidermal cell, and area of abaxial epidermal cell. Model parameters of C-SVM, comprising regularization parameter (C) and kernel parameter (γ), were optimized by cross-validation. The best classification accuracy of the 3 Camellia sections was achieved by the radial basis function SVM classifier (with parameters C = 32, γ = 0.13), as well as the sigmoid SVM classifier (with parameters C = 32, γ = 0.13), which was up to 84.00% in the training set and 90.91% in the prediction set, respectively. Compared with BP-ANN, SVM yields slightly higher prediction accuracy, which indicates that it is feasible to accurately classify the 3 sections of Camellia using SVMs based on leaf anatomy data. © TÜBİTAK.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:34:21Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2013en
dc.identifier.doi10.3906/bot-1210-21en_US
dc.identifier.eissn1303-6106
dc.identifier.issn1300-008X
dc.identifier.urihttp://hdl.handle.net/11693/20745
dc.language.isoEnglishen_US
dc.publisherScientific and Technical Research Council of Turkey - TUBITAKen_US
dc.relation.isversionofhttp://dx.doi.org/10.3906/bot-1210-21en_US
dc.source.titleTurkish Journal of Botanyen_US
dc.subjectBP-ANNen_US
dc.subjectCamelliaen_US
dc.subjectLeaf anatomyen_US
dc.subjectPlant numerical taxonomyen_US
dc.subjectSupervised pattern recognitionen_US
dc.subjectSVMen_US
dc.titleClassification of Camellia species from 3 sections using leaf anatomical data with back-propagation neural networks and support vector machinesen_US
dc.typeArticleen_US

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