



@article {2340,
	title = {Dissimilarity-based classification of chromatographic profiles},
	journal = {Pattern Analysis and ApplicationsPattern Anal. Appl.},
	volume = {11},
	year = {2008},
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},
	month = {2008///},
	pages = {409 - 423},
	abstract = {This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10\% for the four-class problem. {\textcopyright} 2008 Springer-Verlag London Limited.},
	keywords = {Chromatographic images, Dissimilarity representation, Nearest neighbour classifiers, Prototype selection},
	isbn = {14337541 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50549105086\&partnerID=40\&md5=2a5bd02f0caacb5e7ccb1267a861db75},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
