



@book {2398,
	title = {The class imbalance problem in TLC image classification},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4142 LNCS},
	year = {2006},
	note = {Conference code: 68310Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Instituto Superior de Engenharia do Porto, Rua Dr. Antonio Bernardino de A. 431, 4200-072 Porto, Portugal; email: ats@isep.ipp.ptReferences: Kubat, M., Holte, R., Matwin, S., Learning when negative examples abound (1997) European Conference on Machine Learning, pp. 146-153;Lazarevi{\'c}, A., Srivastava, J., Kumar, V., Data mining for analysis of rare events: A case study in security, financial and medical applications (2004) PAKDD-2004; 
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Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special issue on learning from imbalanced data sets (2004) Sigkdd Explorations, 6 (1), pp. 1-6; 
Japkowicz, N., Class imbalances: Are we focusing on the right issue? Workshop on Learning from Imbalanced Datasets II, p. 6. , (2003) ICML, Washington DC; 
Chawla, N.V., Lazarevi{\'c}, A., Hall, L.O., Bowyer, K.W., SMOTEBoost: Improving the prediction of minority class in boosting (2003) 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 107-119; 
Domingos, P., MetaCost: A general method for making classifiers cost-sensitive (1999) Conference on Knowledge Discovery in Data Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155-164; 
Fan, W., Stolfo, S., Zhang, J., Chan, P., Adacost: Misclassication cost-sensitive boosting (1999) Proceedings of Sixteenth International Conference on Machine Learning, pp. 983-990; 
Tax, D.M.J., One-class classification; Concept-learning in the absence of counterexamples (2001) ASCI Dissertation Series, 65, pp. 1-190. , PhD thesis Delft University of Technology; 
Ridder, D., Tax, D.M.J., Duin, R.P.W., An experimental comparison of one-class classification methods (1998) Proc ASCI{\textquoteright}98, 4th Annual Conf of the Advanced School for Computing and Imaging, pp. 213-218. , (Lommel, Belgium, June 9-11), ASCI, Delft; 
Lazarevic, A., Ertoz, L., Ozgur, A., Srivastava, J., Kumar, V., A comparative study of anomaly detection schemes in network intrusion detection (2003) Proceedings of Third SIAM Conference on Data Mining, , San Francisco; 
Weiss, G.M., Mining with rarity: A unifying framework (2004) Sigkdd Explorations, 6 (1), pp. 7-19; 
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Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) Workshop on Learning from Imbalanced Datasets II, , ICML, Washington DC; 
Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) LNCS, 3212. , Springer; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Aguiar, R., Miranda, C.S., Feature extraction for classification of thin-layer chromatography images (2005) LNCS, 3656. , Springer; 
Japkowicz, N., Learning from imbalanced data sets: A comparison of various strategies (2000) Proceedings of Learning from Imbalanced Data, pp. 10-15; 
Kubat, M., Holte, R., Matwin, S., Machine learning for the detection of oil spills in satellite radar images (1998) Machine Learning, 30, pp. 195-215; 
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Heijden, F., Robert, P.W.D., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation, , Wiley; 
Demant, C., Streicher-Abel, B., Waszkewitz, P., (1999) Industrial Image Processing, , Springer-Verlag; 
Chawla, N.V., Bowyer, K., Hall, L., Kegelmeyer, W., SMOTE: Synthetic minority over-sampling technique (2002) Jounal of Artificial Intelligence Research, 16, pp. 321-357
},
	month = {2006///},
	pages = {513 - 523},
	address = {Povoa de Varzim},
	abstract = {The paper presents the methodology developed to solve the class imbalanced problem that occurs in the classification of Thin-Layer Chromatography (TLC) images. The proposed methodology is based on resampling, and consists in the undersampling of the majority class (normal class), while the minority classes, which contain Lysosomal Storage Disorders (LSD) samples, are oversampled with the generation of synthetic samples. For image classification two approaches are presented, one based on a hierarchical classifier and another uses a multiclassifier system, where both classifiers are trained and tested using balanced data sets. The results demonstrate a better performance of the multiclassifier system using the balanced sets. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Chromatographic analysis, Class imbalanced problem, Classification (of information), Hierarchical systems, Lysosomal Storage Disorders (LSD), Medical imaging, Multiclassifier system, Problem solving, Thin-Layer Chromatography (TLC) images},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749667604\&partnerID=40\&md5=3b4a717035de29f4bed59a04613836cd},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
