



@article {3282,
	title = {Can artificial neural networks be used to predict the origin of ozone episodes?},
	journal = {Science of the Total Environment},
	volume = {488-489},
	year = {2014},
	note = {Export Date: 21 November 2014},
	month = {2014},
	pages = {197 - 207},
	abstract = {Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere-Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model - the Multilayer Perceptron - is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, 7Be activity and meteorological conditions were used. With this model, 2-7\% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65-0.92). Precision and F1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures. {\textcopyright} 2014 Elsevier B.V.},
	keywords = {artificial neural network, Classification, Human health, Ozone, Stratosphere, Troposphere},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84900308360\&partnerID=40\&md5=5bb79ec9cf75f45b1556274bf83ce812},
	author = {Fontes, T. and Silva, L.M. and Silva, M. P. and Barros, N. and Carvalho, A. C.}
}
@article {2348,
	title = {Chromatographic pattern classification},
	journal = {IEEE Transactions on Biomedical EngineeringIEEE Trans. Biomed. Eng.},
	volume = {55},
	year = {2008},
	note = {Cited By (since 1996): 5Export Date: 21 November 2012Source: ScopusArt. No.: 8CODEN: IEBEAdoi: 10.1109/TBME.2008.919852PubMed ID: 18714832Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica (INEB), Instituto Superior de Engenharia Do Porto, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptChemicals/CAS: Hydrolases, EC 3.-References: Keir, G., Winchester, B., Clayton, P., Carbohydrate-deficient glycoprotein syndromes: Inborn errors of protein glycosylation (1999) Ann. Clin. Biochem, 36 (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; 
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},
	month = {2008///},
	pages = {1687 - 1696},
	abstract = {In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population. {\textcopyright} 2008 IEEE.},
	keywords = {Acoustic variables control, Algorithms, article, Chromatographic analysis, Chromatographic pattern features, Chromatographic profiles, Chromatography, Chromatography, Thin Layer, Classification, Diagnosis, Computer-Assisted, Gaussian noise (electronic), Hierarchical classifier, Humans, Hydrolases, imaging, Lysosomal Storage Diseases, lysosome storage disease, mathematical analysis, nerve degeneration, noise, normal distribution, Pattern recognition, Pattern Recognition, Automated, Reproducibility of Results, sensitivity and specificity, Urinalysis},
	isbn = {00189294 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44349183146\&partnerID=40\&md5=df677f44d528c936310d8c547624a550},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2335,
	title = {Data classification with multilayer perceptrons using a generalized error function},
	journal = {Neural NetworksNeural Netw.},
	volume = {21},
	year = {2008},
	note = {Cited By (since 1996): 6Export Date: 21 November 2012Source: ScopusCODEN: NNETEdoi: 10.1016/j.neunet.2008.04.004PubMed ID: 18572384Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; INEB-Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Baum, E.B., Wilczek, F., Supervised learning of probability distributions by neural networks (1987) NIPS, pp. 52-61. , Anderson D.Z. (Ed), American Institute of Physics;Bishop, C., (1995) Neural networks for pattern recognition, , Oxford University Press; 
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},
	month = {2008///},
	pages = {1302 - 1310},
	abstract = {The learning process of a multilayer perceptron requires the optimization of an error function E (y, t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E                        Exp, inspired by the Z-EDM algorithm that we have recently proposed. An important property of E
                        Exp is its ability to emulate the behavior of other error functions by the sole adjustment of a real-valued parameter. In other words, E
                        Exp is a sort of generalized error function embodying complementary features of other functions. The experimental results show that the flexibility of the new, generalized, error function allows one to obtain the best results achievable with the other functions with a performance improvement in some cases. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.
},
	keywords = {algorithm, Algorithms, analytical error, article, calculation, Classification, Complementary features, Cybernetics, data analysis, Data classification, Data classifications, Data Interpretation, Statistical, Electric loads, entropy, Error functions, learning, Learning processes, mathematical analysis, Mathematical properties, Models, Statistical, Multilayer neural networks, Multilayer perceptron, Multilayer perceptrons, Multilayers, Neural networks, Neural Networks (Computer), Pattern recognition systems, perceptron, Performance improvements, priority journal, Probability density function},
	isbn = {08936080 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-54449100428\&partnerID=40\&md5=30f8de18c0272f4a440395045bd3c164},
	author = {Silva, L.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
@article {2405,
	title = {Error entropy in classification problems: A univariate data analysis},
	journal = {Neural ComputationNeural Comp.},
	volume = {18},
	year = {2006},
	note = {Cited By (since 1996): 7Export Date: 21 November 2012Source: Scopusdoi: 10.1162/neco.2006.18.9.2036PubMed ID: 16846386Language of Original Document: EnglishCorrespondence Address: Silva, L.M.; Institute de Engenharia Biom{\'e}dica, Laborat{\'o}rio Sinal e Imagem Biom{\'e}dica, 4200-465, Porto, Portugal; email: lmsilva@fe.up.ptReferences: Devroye, L., Gyorfi, L., Lugosi, G., (1996) A Probabilistic Theory of Pattern Recognition, , Berlin: Springer-Verlag;Erdogmus, D., Principe, J., Comparison of entropy and mean square error criteria in adaptive system training using higher order statistics (2000) Proceedings of the Intl. Conf., on ICA and Signal Separation, pp. 75-80. , Helsinki, Finland; 
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},
	month = {2006///},
	pages = {2036 - 2061},
	abstract = {Entropy-based cost functions are enjoying a growing attractiveness in unsupervised and supervised classification tasks. Better performances in terms both of error rate and speed of convergence have been reported. In this letter, we study the principle of error entropy minimization (EEM) from a theoretical point of view. We use Shannon{\textquoteright}s entropy and study univariate data splitting in two-class problems. In this setting, the error variable is a discrete random variable, leading to a not too complicated mathematical analysis of the error entropy. We start by showing that for uniformly distributed data, there is equivalence between the EEM split and the optimal classifier. In a more general setting, we prove the necessary conditions for this equivalence and show the existence of class configurations where the optimal classifier corresponds to maximum error entropy. The presented theoretical results provide practical guidelines that are illustrated with a set of experiments with both real and simulated data sets, where the effectiveness of EEM is compared with the usual mean square error minimization. {\textcopyright} 2006 Massachusetts Institute of Technology.},
	keywords = {article, Classification, entropy, methodology, Models, Theoretical, normal distribution, theoretical model},
	isbn = {08997667 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33748288090\&partnerID=40\&md5=47b51b3d90bc273df156889a92fef01d},
	author = {Silva, L.M. and Felgueiras, C.S. and Alexandre, L.A. and Marques de S{\'a}, J.}
}
@article {2542,
	title = {On combining classifiers using sum and product rules},
	journal = {Pattern Recognition LettersPattern Recogn. Lett.},
	volume = {22},
	year = {2001},
	note = {Cited By (since 1996): 51Export Date: 21 November 2012Source: Scopusdoi: 10.1016/S0167-8655(01)00073-3Language of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; INEB-Inst. de Engenharia Biomedica, Rua Dr. Roberto Frias s/n, Porto 4200-465, Portugal; email: lfbaa@noe.ubi.ptReferences: Alexandre, L., Campilho, A., Kamel, M., Combining independent and unbiased classifiers using weighted average (2000) Proc. 15th Internat. Conf. on Pattern Recognition, 2. , IEEE Press, Barcelona, Spain;Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford University Press, Oxford; 
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},
	month = {2001///},
	pages = {1283 - 1289},
	abstract = {This paper presents a comparative study of the performance of arithmetic and geometric means as rules to combine multiple classifiers. For problems with two classes, we prove that these combination rules are equivalent when using two classifiers and the sum of the estimates of the a posteriori probabilities is equal to one. We also prove that the case of a two class problem and a combination of two classifiers is the only one where such equivalence occurs. We present experiments illustrating the equivalence of the rules under the above mentioned assumptions. {\textcopyright} 2001 Elsevier Science B.V. All rights reserved.},
	keywords = {artificial neural network, Classification, Classifier fusion, Combinatorial mathematics, Combining classifiers, Equivalence classes, Estimation, K nearest-neighbours, Multiple classifiers, Neural networks, Pattern recognition, probability},
	isbn = {01678655 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0034875843\&partnerID=40\&md5=becfe9c0c4a7d97f7bbaf33f3059ef8f},
	author = {Alexandre, L.A. and Campilho, A.C. and Kamel, M.}
}
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	title = {Classification of foetal heart rate sequences based on fractal features},
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	note = {Cited By (since 1996): 13Export Date: 21 November 2012Source: ScopusCODEN: MBECDdoi: 10.1007/BF02510743PubMed ID: 9684460Language of Original Document: EnglishCorrespondence Address: Felguiras, C.S.; Instituto de Engenharia Biomedica, Praca Coronel Pacheco 1, 4050 Porto, PortugalReferences: Bernardes, J., (1993) Leitura Automatizada Do Cardiotocograma. Desenvolvimento e Availia{\c c}{\`a}o, , Ph.D. thesis, Faculdade de Medicina da Universidade do Porto, Portugal;Bernardes, J., Moura, C., Marques De S{\'a}, J.P., Pereira Leite, L., Van Geijn, H.P., The Porto system (1994) A Critical Appraisal of Foetal Surveillance, pp. 315-324. , VAN GEIJN H. P. and COPRAY, F. J. A. (Eds.) Elsevier Science, Amsterdam; 
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},
	month = {1998///},
	pages = {197 - 201},
	publisher = {Peter Peregrinus Ltd},
	abstract = {Visual inspection of foetal heart rate (FHR) sequences is an important means of foetal well-being evaluation. The application of fractal features for classifying physiologically relevant FHR sequence patterns is reported. The use of fractal features is motivated by the difficulties exhibited by traditional classification schemes to discriminate some classes of FHR sequence and by the recognition that this type of signal exhibits features on different scales of observation, just as fractal signals do. To characterise the signals by fractal features, two approaches are taken. The first models the FHR sequences as temporal fractals. The second uses techniques from the chaos-theory field and aims to model the attractor based on FHR sequences. The fractal features determined by both approaches are used to design a Bayesian classification scheme. Classification results for three classes are presented; they are quite satisfactory and illustrate the importance of this type of methodology.Visual inspection of foetal heart rate (FHR) sequences is an important means of foetal well-being evaluation. The application of fractal features for classifying physiologically relevant FHR sequence patterns is reported. The use of fractal features is motivated by the difficulties exhibited by traditional classification schemes to discriminate some classes of FHR sequence and by the recognition that this type of signal exhibits features on different scales of observation, just as fractal signals do. To characterise the signals by fractal features, two approaches are taken. The first models the FHR sequences as temporal fractals. The second uses techniques from the chaos-theory field and aims to model the attractor based on FHR sequences. The fractal features determined by both approaches are used to design a Bayesian classification scheme. Classification results for three classes are presented; they are quite satisfactory and illustrate the importance of this type of methodology.
},
	keywords = {article, Bayes Theorem, Cardiology, Chaos techniques, Chaos theory, Classification, Computer aided analysis, Computer simulation, Electrocardiography, Female, Fetal monitoring, fetus, fetus heart rate, FHR classification, FHR sequence, Foetal heart rate sequences, fractal analysis, Fractal features, Fractals, Heart Rate, Fetal, human, Humans, Pattern recognition, Pregnancy, Signal Processing, Computer-Assisted, technique},
	isbn = {01400118 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0032033010\&partnerID=40\&md5=563144fcdb064dd777002155aaa62f73},
	author = {Felgueiras, C.S. and De Marques S{\'a}, J.P. and Bernardes, J. and Gama, S.}
}
