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Citez

[1] UCI machine learning repository.Search in Google Scholar

[2] National Cancer Registry, The Maria Skłodowska–Curie memorial Cancer Center, Department of Epidemiology and Cancer Prevetion, December 2013.Search in Google Scholar

[3] TNM breast cancer staging, December 2014.Search in Google Scholar

[4] M.N. Ahmed, S.M. Yamany, N. Mohamed, A.A. Farag, and T. Moriarty. A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Transactions on Medical Imaging, 21:193–199, 2002.10.1109/42.99633811989844Search in Google Scholar

[5] J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.10.1007/978-1-4757-0450-1Search in Google Scholar

[6] C.M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.Search in Google Scholar

[7] H.J.G. Bloom and W.W. Richardson. Histological grading and prognosis in breast cancer. British Journal of Cancer, 11:359–377, 1957.10.1038/bjc.1957.43207388513499785Search in Google Scholar

[8] J.C. Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3:32–57, 1973.10.1080/01969727308546046Search in Google Scholar

[9] A. Ethem. Introduction to Machine Learning. MIT Press, Boston, 2010.Search in Google Scholar

[10] J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, and F. Bray. Cancer incidence and mortality worldwide. IARC Cancer Base, No. 11, 2012.Search in Google Scholar

[11] P. Filipczuk, T. Fevens, A. Krzyzak, and R. Monczak. Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Transactions on Medical Imaging, PP(99):1–1, 2013.10.1109/TMI.2013.227515123912498Search in Google Scholar

[12] P. Filipczuk, M. Kowal, and A. Obuchowicz. Fuzzy clustering and adaptive thresholding based segmentation method for breast cancer diagnosis. Computer Recognition Systems, 4(5):613–622, 2011.10.1007/978-3-642-20320-6_64Search in Google Scholar

[13] D.L. Fisher. Data, documentation and decision tables. Comm ACM, 9(1):26–31, 1966.10.1145/365153.365163Search in Google Scholar

[14] Y.M. George, H.H. Zayed, M.I. Roushdy, and B.M. Elbagoury. Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Systems Journal, PP(99):1–16, 2013.Search in Google Scholar

[15] T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning, 2nd. edition. Springer, New York, 2009.10.1007/978-0-387-84858-7Search in Google Scholar

[16] S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, 1998.Search in Google Scholar

[17] R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1):63–90, 1993.10.1023/A:1022631118932Search in Google Scholar

[18] T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 881–892, 2002.10.1109/TPAMI.2002.1017616Search in Google Scholar

[19] S.B. Kotsiantis. Supervised machine learning: A review of classification techniques. Informatica, pages 249–268, 2007.Search in Google Scholar

[20] B. Krawczyk and P. Filipczuk. Cytological image analysis with firefly nuclei detection and hybrid one–class classification decomposition. Engineering Applications of Artificial Intelligence, 31:126–135, 2014.10.1016/j.engappai.2013.09.017Search in Google Scholar

[21] B. Krawczyk, Ł. Jeleń, A. Krzyżak, and T. Fevens. Oversampling methods for classification of imbalanced breast cancer malignancy data. Lecture Notes in Computer Science (LNCS), 7594:483–490, 2012.10.1007/978-3-642-33564-8_58Search in Google Scholar

[22] B. Krawczyk and G. Schaefer. A hybrid classifier committee for analysing asymmetry features in breast thermograms. Applied Soft Computing, 20:112–118, 2014.10.1016/j.asoc.2013.11.011Search in Google Scholar

[23] Jihene Malek, Abderrahim Sebri, Souhir Mabrouk, Kholdoun Torki, and Rached Tourki. Automated breast cancer diagnosis based on gvf-snake segmentation, wavelet features extraction and fuzzy classification. Journal of Signal Processing Systems, 55(1-3):49–66, 2009.10.1007/s11265-008-0198-2Search in Google Scholar

[24] O.L. Mangasarian, R. Setiono, and W.H. Wolberg. Pattern Recognition via Linear Programming: Theory and Application to Medical Diagnosis. Large-Scale Num. Opt., Philadelphia: SIAM, pages 22–31, 1990.Search in Google Scholar

[25] A. Marcano-Cedeño, J. Quintanilla-Domínguez, and D. Andina. WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications, 38(8):9573 – 9579, 2011.10.1016/j.eswa.2011.01.167Search in Google Scholar

[26] T. Mitchell. Machine Learning, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression (Draft Version). McGraw Hill, 2005.Search in Google Scholar

[27] S.I. Niwas, P. Palanisamy, and K. Sujathan. Wavelet based feature extraction method for breast cancer cytology images. In IEEE Symposium on Industrial Electronics Applications (ISIEA), pages 686–690, Oct 2010.10.1109/ISIEA.2010.5679377Search in Google Scholar

[28] J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.Search in Google Scholar

[29] R.L. Rivest. Learning decision lists. Machine Learning, 2:229–246, 1987.10.1007/BF00058680Search in Google Scholar

[30] J.B.T.M Roerdink and A. Meijster. The watershed transform: definitions, algorithms, and parallelization strategies. Fundamenta Informaticae, 41:187–228, 2000.Search in Google Scholar

[31] W.N. Street, W.H. Wolberg, and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. In IS&T/SPIE Inter. Symp. on Electronic Imaging: Science and Technology, volume 1905, pages 861–870, 1993.10.1117/12.148698Search in Google Scholar

[32] W.H Wolberg and O.L. Mangasarian. Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology. Proceedings of National Academy of Science, USA, 87:9193–9196, 1990.10.1073/pnas.87.23.9193551302251264Search in Google Scholar

[33] Xiangchun Xiong, Yangon Kim, Yuncheol Baek, Dae Wong Rhee, and Soo-Hong Kim. Analysis of breast cancer using data mining & statistical techniques. In Proc. 6th Int. Conf. on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and 1st ACIS Int. Worksh. on Self-Assembling Wireless Networks, pages 82–87, 2005.Search in Google Scholar

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