Classifier Selection Based On The Correlation Of Diversity Measures: When Fewer Is More

dc.creatorFaria F.A.
dc.creatorDos Santos J.A.
dc.creatorSarkar S.
dc.creatorRocha A.
dc.creatorTorres R.D.S.
dc.date2013
dc.date2015-06-25T19:19:26Z
dc.date2015-11-26T15:17:24Z
dc.date2015-06-25T19:19:26Z
dc.date2015-11-26T15:17:24Z
dc.date.accessioned2018-03-28T22:27:08Z
dc.date.available2018-03-28T22:27:08Z
dc.identifier9780769550992
dc.identifierBrazilian Symposium Of Computer Graphic And Image Processing. , v. , n. , p. 16 - 23, 2013.
dc.identifier15301834
dc.identifier10.1109/SIBGRAPI.2013.12
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84891516737&partnerID=40&md5=3fe0f7a8259c5066f34ec023529fe4c1
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/89956
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/89956
dc.identifier2-s2.0-84891516737
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1259445
dc.descriptionThe ever-growing access to high-resolution images has prompted the development of region-based classification methods for remote sensing images. However, in agricultural applications, the recognition of specific regions is still a challenge as there could be many different spectral patterns in a same studied area. In this context, depending on the features used, different learning methods can be used to create complementary classifiers. Many researchers have developed solutions based on the use of machine learning techniques to address these problems. Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). In MCS, diversity becomes an essential factor for their success. Different works have been using diversity measures to select appropriate high-performance classifiers, but the challenge of finding the optimal number of classifiers for a target task has not been properly addressed yet. In general, the proposed solutions rely on the a priori use of ad hoc strategies for selecting classifiers, followed by the evaluation of their effectiveness results during training. Searching by the optimal number of classifiers, however, makes the selection process more expensive. In this paper, we address this issue by proposing a novel strategy for selecting classifiers to be combined based on the correlation of different diversity measures. Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. A fusion framework has been used in our experiments targeted to the classification of coffee crops in remote sensing images. Experiment results demonstrate that the novel strategy is able to yield comparable effectiveness results when contrasted to several baselines, but using much fewer classifiers. © 2013 IEEE.
dc.description
dc.description
dc.description16
dc.description23
dc.descriptionMicrosoft Research
dc.descriptionCastillejo-González, López-Granados, García-Ferrer, Peña-Barragán, Jurado-Expósito, De La Orden, González-Audicana, Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using quickbird imagery (2009) Elsevier Computer and Electronics in Agriculture
dc.descriptionDos Santos, J.A., Faria, F.A., Calumby, R., Da S Torres, R., Lamparelli, R., A genetic programming approach for coffee crop recognition (2010) IEEE Geoscience and Remote Sensing Symposium
dc.descriptionPouteau, R., Stoll, B., Svm selective fusion (self) for multi-source classification of structurally complex tropical rainforest (2012) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (4), pp. 1203-1212. , aug
dc.descriptionDuro, D.C., Franklin, S.E., Dubé, M.G., A comparison of pixelbased and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery (2012) Remote Sensing of Environment, 118, pp. 259-272
dc.descriptionBlaschke, T., Object based image analysis for remote sensing (2010) ISPRS Journal of Photogrammetry and Remote Sensing
dc.descriptionDos Santos, J.A., Penatti, O.A.B., Da S Torres, R., Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification (2010) Intl. Conf. on Computer Vision Theory and Applications, pp. 203-208. , Angers, France, May
dc.descriptionRocha, A., Papa, J., Meira, L., How far you can get using machine learning black-boxes (2010) Conf. on Graphics, Patterns and Images, pp. 193-200. , 30 2010-sept. 3
dc.descriptionLu, D., Weng, Q., A survey of image classification methods and techniques for improving classification performance (2007) Intl. Journal of Remote Sensing, 28 (5), pp. 823-870
dc.descriptionMountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: A review (2011) ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), pp. 247-259
dc.descriptionFaria, F.A., Dos Santos, J.A., Torres, R.D.S., Rocha, A., Falcão, A.X., Automatic fusion of region-based classifiers for coffee crop recognition (2012) IEEE Geoscience and Remote Sensing Symposium
dc.descriptionKuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy (2003) Machine Learning
dc.descriptionShipp, C.A., Kuncheva, L.I., An investigation into how adaboost affects classifier diversity (2009) IPMU, pp. 203-208
dc.descriptionFaria, F.A., Santos, J.A., Rocha, A., Torres, R.D.S., Automatic classifier fusion for produce recognition (2012) Conf. on Graphics, Patterns and Images
dc.descriptionDos Santos, J.A., Faria, F.A., Torres, R.D.S., Rocha, A., Gosselin, P.-H., Philipp-Foliguet, S., Falcao, A., Descriptor correlation analysis for remote sensing image multi-scale classification (2012) Intl. Conf. on Pattern Recognition, pp. 3078-3081
dc.descriptionDu, P., Xia, J., Zhang, W., Tan, K., Liu, Y., Liu, S., Multiple classifier system for remote sensing image classification: A review (2012) Sensors, 12 (4), p. 4764
dc.descriptionBoser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for optimal margin classifiers (1992) Workshop on Computational Learning Theory, Ser. COLT '92, pp. 144-152
dc.descriptionCristianini, N., Shawe-Taylor, J., (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, , Cambridge University Press
dc.descriptionGuigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis (2006) Intl. Journal of Computer Vision
dc.descriptionMyneni, R.B., Hall, F.G., Sellers, P.J., Marshak, A.L., The interpretation of spectral vegetation indexes (1995) Geoscience and Remote Sensing, IEEE Transactions on, 33 (2), pp. 481-486
dc.descriptionStehling, R., Nascimento, M., Falcao, A., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) ACM Conf. on Information and Knowledge Management, pp. 102-109
dc.descriptionPass, G., Zabih, R., Miller, J., Comparing images using color coherence vectors (1996) ACM Intl. Conf. on Multimedia, pp. 65-73
dc.descriptionSwain, M., Ballard, D., Color indexing (1991) Intl. Journal of Computer Vision, 7 (1), pp. 11-32
dc.descriptionHuang, C., Liu, Q., An orientation independent texture descriptor for image retrieval (2007) Intl. Conf. on Communications, Circuits and Systems, pp. 772-776
dc.descriptionZegarra, J., Leite, N., Torres, R., Wavelet-based feature extraction for fingerprint image retrieval (2008) Journal of Computational and Applied Mathematics, 227 (2), pp. 294-307
dc.descriptionUnser, M., Sum and difference histograms for texture classification (1986) IEEE Transactions Pattern Analysis and Machine Intelligence, 8 (1), pp. 118-125
dc.descriptionPenatti, O.A.B., Valle, E., Torres, R.D.S., Comparative study of global color and texture descriptors for web image retrieval (2012) Journal of Visual Communication and Image Representation
dc.descriptionBrennan, R.L., Prediger, D.J., Coefficient kappa: Some uses, misuses, and alternatives (1981) Educational and Psychological Measurement
dc.descriptionMa, Z., Redmond, R.L., Tau coefficients for accuracy assessment of classification of remote sensing data (1995) Photogrametric Engineering and Remote Sensing
dc.descriptionKendall, M.G., A new measure of rank correlation (1938) Biometrika, 30 (1-2), pp. 81-93. , Jun
dc.languageen
dc.publisher
dc.relationBrazilian Symposium of Computer Graphic and Image Processing
dc.rightsfechado
dc.sourceScopus
dc.titleClassifier Selection Based On The Correlation Of Diversity Measures: When Fewer Is More
dc.typeActas de congresos


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