Optimum Path Forest Classifier Applied To Laryngeal Pathology Detection

dc.creatorPapa J.P.
dc.creatorSpadotto A.A.
dc.creatorFalcao A.X.
dc.creatorPereira J.C.
dc.date2008
dc.date2015-06-30T19:21:36Z
dc.date2015-11-26T14:44:03Z
dc.date2015-06-30T19:21:36Z
dc.date2015-11-26T14:44:03Z
dc.date.accessioned2018-03-28T21:52:34Z
dc.date.available2018-03-28T21:52:34Z
dc.identifier9788022728560
dc.identifierProceedings Of Iwssip 2008 - 15th International Conference On Systems, Signals And Image Processing. , v. , n. , p. 249 - 252, 2008.
dc.identifier
dc.identifier10.1109/IWSSIP.2008.4604414
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-52949127394&partnerID=40&md5=c495dc2b237e943858099d9ded76012d
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/105899
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/105899
dc.identifier2-s2.0-52949127394
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1251954
dc.descriptionOptimum path forest-based classifiers are a novel approach for supervised pattern recognition. The OPF classifier differs from traditional approaches by not estimating probability density functions of the classes neither assuming samples linearity, and creates a discrete optimal partition of the feature space, in which the decision boundary is obtained by the influence zones of the most representative samples of the training set. Due to the large number of applications in biomedical signal processing involving pattern recognition techniques, specially voice disorders identification, we propose here the laryngeal pathology detection by means of OPF. Experiments were performed in three public datasets against SVM, and a comparison in terms of accuracy rates and execution times was also regarded.
dc.description
dc.description
dc.description249
dc.description252
dc.descriptionA.A. Spadotto, J.P. Papa, A.R. Gatto, P.C. Cola, J.C. Pereira, R.C. Guido, and A.O. Schelp, Denoising swallowing sound to improve the evaluators qualitative analysis., Computers and Electrical Engineering: Advances on Computer-based Biological Signal Processing Techniques, 34, no. 2, pp. 148-153, 2008Spadotto, A.A., Pereira, J.C., Guido, R.C., Papa, J.P., Falcão, A.X., Gatto, A.R., Cola, P.C., Shelp, A.O., Oropharyngeal dysphagia identification using wavelets and optimum path forest (2008) Proceedings of the 3th IEEE International Symposium on Communications, Control and Signal Processing, , to appear
dc.descriptionHadjitodorov, S.T., Boyanov, B., Teston, B., Laryngeal pathology detection by means of class-specific neural maps (2000) IEEE Transactions on Information Technology in Biomedicine, 4 (1), pp. 68-73
dc.descriptionBoyanov, B., Hadjitodorov, S.T., Acoustic analysis of pathological voices. a voice analysis systemfor the screening of laryngeal diseases (1997) IEEE Transactions on Engineering in Medicine and Biology Magazine, 16 (4), pp. 74-82
dc.descriptionGodino-Llorente, J.I., Vilda, P.G., Senz-Lechn, N., Blanco-Velasco, M., Craz-Roldn, F., Ferrer-Ballester, M.A., (2005) Support vector machines applied to the detection of voice disorders, 3817, pp. 219-230
dc.descriptionPerrin, E., Berger-Vachon, C., Kauffmann, I., Collet, L., (2006) Acoustical recognition of laryngeal pathology using the fundamental frequency and the first three formants of vowels, 35 (4), pp. 361-368
dc.descriptionMezzalama, M., Prinetto, P., Morra, B., (2006) Experiments in automatic classification of laryngeal pathology, 21 (5), pp. 603-611
dc.descriptionHadjitodorov, S.T., Ivanov, T., Boyanov, B., Analysis of dysphony using objective voice parameter (1993) Proceedings of the II Balkan Conference on Operational Research, pp. 911-917
dc.descriptionSchlotthauer, G., Torres, M.E., Jackson-Menaldi, C., Automatic diagnosis of pathological voices (2006) Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing, pp. 150-155
dc.descriptionBoser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for-optimal margin classifiers (1992) Proc. 5th Workshop on Computational Learning Theory, pp. 144-152. , New York, NY, USA, ACM Press
dc.descriptionDuan, K., Keerthi, S.S., Which is the best multiclass svm method? an empirical study (2005) Multiple Classifier Systems, pp. 278-285
dc.descriptionPapa, J.P., Falcão, A.X., Miranda, P.A.V., Suzuki, C.T.N., Mascarenhas, N.D.A., Design of robust pattern classifiers based on optimum-path forests (2007) Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), pp. 337-348. , MCT/INPE
dc.descriptionPapa, J.P., Falcão, A.X., Suzuki, C.T.N., Mascarenhas, N.D.A., A discrete approach for supervised pattern recognition (2008) 12th International Workshop on Combinatorial Image Analysis (IWCIA), 4958, pp. 136-147. , Springer
dc.descriptionJ.A. Montoya-Zegarra, J.P. Papa, N.J. Leite, R.S. Torres, and A.X. Falcão, Rotation-invariant texture recognition, in 3rd International Symposium on Visual Computing, Lake Tahoe, Nevada, CA, USA, Nov 2007, Part II, LNCS 4842, pp. 193-204, SpringerFalcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on PAMI, 26 (1), pp. 19-29. , Jan
dc.descriptionAllène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., Some links between min-cuts, optimal spanning forests and watersheds (2007) Proceedings of the ISMM'08, pp. 253-264
dc.descriptionHadjitodorov, S.T., Mitev, P., Boyanov, B., (2005) Laryngeal databases, , http://www.informatics.bangor.ac.uk/~kuncheva, Available in
dc.descriptionCohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20, pp. 37-46
dc.descriptionChang, C.C., Lin, C.J., (2001) LIBSVM: A library for support vector machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, Software available at url
dc.languageen
dc.publisher
dc.relationProceedings of IWSSIP 2008 - 15th International Conference on Systems, Signals and Image Processing
dc.rightsfechado
dc.sourceScopus
dc.titleOptimum Path Forest Classifier Applied To Laryngeal Pathology Detection
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución