1. bookVolumen 10 (2020): Edición 2 (April 2020)
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eISSN
2449-6499
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30 Dec 2014
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Combining Classifiers for Foreign Pattern Rejection

Publicado en línea: 20 Mar 2020
Volumen & Edición: Volumen 10 (2020) - Edición 2 (April 2020)
Páginas: 75 - 94
Recibido: 09 Oct 2019
Aceptado: 16 Feb 2020
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
Idiomas
Inglés

[1] W. Homenda and A. Jastrzebska, Global, local and embedded architectures for multiclass classification with foreign elements rejection: an overview, Proc. of the 7th International Conference of Soft Computing and Pattern Recognition, pp. 89–94, 2015.10.1109/SOCPAR.2015.7492789Search in Google Scholar

[2] F. J. Anscombe, Rejection of outliers, Technometrics, vol. 2, no. 2, pp. 123–147, 1960.10.1080/00401706.1960.10489888Search in Google Scholar

[3] V. Barnett and T. Lewis, Outliers in Statistical Data, 3rd ed. Wiley, 1994.Search in Google Scholar

[4] M. P. Maples, D. E. Reichart, N. C. Konz, T. A. Berger, A. S. Trotter, J. R. Martin, D. A. Dutton, M. L. Paggen, R. E. Joyner, and C. P. Salemi, Robust chauvenet outlier rejection, The Astrophysical Journal Supplement Series, vol. 238, no. 1, p. 2, 2018.10.3847/1538-4365/aad23dSearch in Google Scholar

[5] Z. Li, R. J. Baseman, Y. Zhu, F. A. Tipu, N. Slonim, and L. Shpigelman, A unified framework for outlier detection in trace data analysis, IEEE Transactions on Semiconductor Manufacturing, vol. 27, no. 1, pp. 95–103, 2014.10.1109/TSM.2013.2267937Search in Google Scholar

[6] G. Yuksel and M. Cetin, Outlier detection in a preliminary test estimator of the mean, Journal of Statistics and Management Systems, vol. 19, no. 4, pp. 605–615, 2016.10.1080/09720510.2016.1139851Search in Google Scholar

[7] M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, A review of novelty detection, Signal Processing, vol. 99, pp. 215–249, 2014.10.1016/j.sigpro.2013.12.026Search in Google Scholar

[8] R. Rocci, S. A. Gattone, and R. Di Mari, A data driven equivariant approach to constrained gaussian mixture modeling, Advances in Data Analysis and Classification, vol. 12, no. 2, pp. 235–260, 2018.10.1007/s11634-016-0279-1Search in Google Scholar

[9] A. Punzo, A. Mazza, and A. Maruotti, Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions, Journal of Applied Statistics, vol. 45, no. 14, pp. 2563–2584, 2018.Search in Google Scholar

[10] L. Xiang, K. K. Yau, and A. H. Lee, The robust estimation method for a finite mixture of poisson mixed-effect models, Computational Statistics & Data Analysis, vol. 56, no. 6, pp. 1994–2005, 2012.Search in Google Scholar

[11] H. Otneim and D. Tjøstheim, The locally gaussian density estimator for multivariate data, Statistics and Computing, vol. 27, no. 6, pp. 1595–1616, 2017.Search in Google Scholar

[12] J. Zhang and H. Wang, Detecting outlying subspaces for high- dimensional data: the new task, and performance, Knowledge and Information Systems, vol. 3, no. 10, pp. 333–355, 2006.10.1007/s10115-006-0020-zSearch in Google Scholar

[13] V. Hautamaki, I. Karkkainen, and P. Franti, Outlier detection using k-nearest neighbour graph, Proc. of the 17th International Conference on Pattern Recognition, vol. 3, pp. 430–433, 2004.10.1109/ICPR.2004.1334558Search in Google Scholar

[14] M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander, Lof: identifying density- based local outliers, Proc. of the ACM SIGMOD International Conference on Management of Data, vol. 29, pp. 93–104, 2000.10.1145/335191.335388Search in Google Scholar

[15] H. Izakian and W. Pedrycz, Anomaly detection in time series data using a fuzzy c-means clustering, Proc. of IFSA World Congress and NAFIPS Annual Meeting, pp. 1513–1518, 2013.Search in Google Scholar

[16] F. de Morsier, D. Tuia, M. Borgeaud, V. Gass, and J.-P. Thiran, Cluster validity measure and merging system for hierarchical clustering considering outliers, Pattern Recognition, vol. 48, no. 4, pp. 1478–1489, 2015.Search in Google Scholar

[17] B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, New support vector algorithms, Neural Computation, vol. 12, no. 5, pp. 1207–1245, 2000.Search in Google Scholar

[18] B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001.Search in Google Scholar

[19] C. Gautam, R. Balaji, S. K., A. Tiwari, and K. Ahuja, Localized multiple kernel learning for anomaly detection: One-class classification, Knowledge-Based Systems, vol. 165, pp. 241–252, 2019.10.1016/j.knosys.2018.11.030Search in Google Scholar

[20] C. Desir, S. Bernard, C. Petitjean, and L. Heutte, One class random forests, Pattern Recognition, vol. 46, no. 12, pp. 3490–3506, 2013.Search in Google Scholar

[21] D. M. J. Tax and R. P. W. Duin, Combining one-class classifiers, Proc. of Multiple Classifier Systems: Second International Workshop, pp. 299–308, 2001.10.1007/3-540-48219-9_30Search in Google Scholar

[22] W. Homenda, A. Jastrzebska, and W. Pedrycz, Rejecting foreign elements in pattern recognition problem. reinforced training of rejection level, Proc. of the 7th International Conference on Agents and Artificial Intelligence, pp. 90–99, 2015.10.5220/0005207900900099Search in Google Scholar

[23] Y. Shiraishia and K. Fukumizu, Statistical approaches to combining binary classifiers for multi-class classification, Neurocomputing, vol. 74, pp. 680–688, 2011.10.1016/j.neucom.2010.09.004Search in Google Scholar

[24] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recognition, vol. 8, no. 44, pp. 1761–1776, 2011.Search in Google Scholar

[25] Y. LeCun, C. Cortes, and C. J. Burges, The mnist database of handwritten digits, http://yann.lecun.com/exdb/mnist.Search in Google Scholar

[26] T. E. de Campos, B. R. Babu, and M. Varma, Character recognition in natural images, in Proc. of the International Conference on Computer Vision Theory and Applications, 2009. [Online]. Available: https://www.microsoft.com/enus/research/publication/character-recognition-in-natural-images/Search in Google Scholar

[27] L. Breiman, Random forests, Machine Learning, vol. 1, no. 45, pp. 5–32, 2001.10.1023/A:1010933404324Search in Google Scholar

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