



@book {2343,
	title = {Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_41Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Keir, G., Winchester, B.G., Clayton, P., Carbohydrate-deficient Glycoprotein Syndromes: Inborn Errors of Protein Glycosylate (1999) Ann. Clin. Biochem, 36 (PART 1), pp. 20-36;Durand, G., Seta, N., Protein Glycosylation and Diseases: Blood and Urinary Oligosaccharides as Markers for Diagnosis and Therapeutic Monitoring (2000) Clin. Chem, 46, pp. 795-805; 
Scriver, S.R., Beaudet, A.L., Sly, W.S., Valle, D., (2001) The Metabolic Bases of Inherited Disease, 3. , MCGraw-Hill, New-York; 
Friedman, R.B., Williams, M.A., Moser, H.W., Kolodny, E.H., Improved Thin-Layer Chromatographic Method in the Diagnosis of Mannosidosis (1978) Clinical Chemistry, 24, pp. 1576-1577; 
Kurczynski, T.W., Kendzierski, K.S., Sewell, A.C., Kuczynski, T.W., Urinary Oligosaccharides in Pregnant or Lactating Women: Pitfall in Screening (1993) Clin. Chem, 39, pp. 2346-2347; 
Sewell, A.C., Urinary Oligosaccharides. Techniques in Diagnostic Human (1991) Biochemical Genetics, 219-231; 
Duvdevani-Bar, S., Edelman, S., Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes (1999) Int. J. Comput. Vision, 33, pp. 201-228; 
Pekalska, E., Duin, R.P.W., Dissimilarity Representations Allow for Building Good Classifiers (2002) Pattern Recogn. Lett, 23, pp. 943-956; 
Goldstone, R.L., Son, J.Y., Similarity (2005) Cambridge Handbook of Thinking and Reasoning, pp. 13-36. , Holyoak, K, Morrison, R, eds, Cambridge University Press, Cambridge; 
Pekalska, E., Duin, R.P.W., (2005) The Dissimilarity Representation for Pattern Recognition -Fundations and Applications, 64. , World Scientific Publishing, Singapore; 
Lavine, B.K., Clustering and Classification of Analytical Data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , Meyers, R.A, ed, John Wiley \& Sons, Chichester; 
Kubat, M., Holte, R., Matwin, S.: Learning when Negative Examples Abound. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, 1224, pp. 146-153. Springer, Heidelberg (1997)Skurichina, M., Raudys, S., Duin, R.P.W., K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training (2000) IEEE Transactions on Neural Networks, 11; 
Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special Issue: Learning from Imbalanced Data Sets (2004) Sigkdd Explorations, 6, pp. 1-6; 
Tax, D.M.J., (2001) One-class Classification; Concept-learning in the Absence of Counter-examples, , Ph.D. thesis: Delft University of Technology; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Chromatographic Pattern Classification (2007) IEEE Trans. on Biomedical Engineering, , doi:10.1109/TBME.2008.919852; 
Paclik, P., Duin, R.P.W., Dissimilarity-based Classification of Spectra: Computational Issues (2003) Real-Time Imaging, 9, pp. 237-244; 
Paclik, P., Duin, R.P.W., Classifying Spectral Data using Relational Representation (2003) Proc. of Spectral Imaging Workshop, , Graz, Austria; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Dissimilarity-based Classification of Chromatographic Profiles (2007) Pattern Analysis and Applications, , doi:10.1007/sl0044-008-0113-2; 
Pekalska, E., Paclik, P., Duin, R.P.W., A Generalized Kernel Approach to Dissimilarity-based Classifcation (2001) Journal of Machine Learning Research, 2, pp. 175-211; 
Ripley, B.D., (2004) Pattern Recognition and Neural Networks, , Cambridge University Press, Cambridge; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes Using Fuzzy Sets (2003) Workshop on Learning from Imbalanced Datasets II, ICML, , Washington DC
},
	month = {2008///},
	pages = {413 - 422},
	address = {Povoa de Varzim},
	abstract = {This paper presents a new classification approach to deal with class imbalance in TLC patterns, which is due to the huge difference between the number of normal and pathological cases as a consequence of the rarity of LSD diseases. The proposed architecture is formed by two decision stages: the first is implemented by a one-class classifier aiming at recognizing most of the normal samples; the second stage is a hierarchical classifier which deals with the remaining outliers that are expected to contain the pathological cases and a small percentage of normal samples. We have also evaluated this architecture by a forest of classifiers, using the majority voting as a rule to generate the final classification. The results that were obtained proved that this approach is able to overcome some of the difficulties associated with class imbalance. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Class imbalances, Classification (of information), Classification approach, Classifiers, Heidelberg (CO), Hierarchical classifiers, Image analysis, Imaging techniques, Imbalance problems, International conferences, Learning systems, Majority voting, Pathological cases},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47849088119\&partnerID=40\&md5=df911723c51c6f540287fdd675267725},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
