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Breast Cancer Diagnosis and Prognosis Via Linear Programming

Citations

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Cited by:

  1. Valeriy Gavrishchaka & Supriya Banerjee, 2006. "Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting," Computational Management Science, Springer, vol. 3(2), pages 147-160, April.
  2. Cinzia Viroli, 2010. "Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 363-388, November.
  3. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
  4. W. Art Chaovalitwongse & Ya-Ju Fan & Rajesh C. Sachdeo, 2008. "Novel Optimization Models for Abnormal Brain Activity Classification," Operations Research, INFORMS, vol. 56(6), pages 1450-1460, December.
  5. Sung, Bongjung & Lee, Jaeyong, 2023. "Covariance structure estimation with Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  6. P. S. Bradley & Usama M. Fayyad & O. L. Mangasarian, 1999. "Mathematical Programming for Data Mining: Formulations and Challenges," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 217-238, August.
  7. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
  8. W. N. Street & O. L. Mangasarian, 1998. "Improved Generalization via Tolerant Training," Journal of Optimization Theory and Applications, Springer, vol. 96(2), pages 259-279, February.
  9. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
  10. Jun-Ya Gotoh & Michael Jong Kim & Andrew E. B. Lim, 2017. "Calibration of Distributionally Robust Empirical Optimization Models," Papers 1711.06565, arXiv.org, revised May 2020.
  11. A. Astorino & M. Gaudioso, 2002. "Polyhedral Separability Through Successive LP," Journal of Optimization Theory and Applications, Springer, vol. 112(2), pages 265-293, February.
  12. Yuhong Wei & Paul McNicholas, 2015. "Mixture model averaging for clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(2), pages 197-217, June.
  13. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
  14. Akhil Kumar & Ignacio Olmeda, 1999. "A Study of Composite or Hybrid Classifiers for Knowledge Discovery," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 267-277, August.
  15. Wang, Wan-Lun, 2015. "Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 223-235.
  16. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
  17. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
  18. Vijayalakshmi S & John A & Sunder R & Senthilkumar Mohan & Sweta Bhattacharya & Rajesh Kaluri & Guang Feng & Usman Tariq, 2020. "Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
  19. Eva K. Lee & Richard J. Gallagher & David A. Patterson, 2003. "A Linear Programming Approach to Discriminant Analysis with a Reserved-Judgment Region," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 23-41, February.
  20. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
  21. Balakrishnan, N. & Capitanio, A. & Scarpa, B., 2014. "A test for multivariate skew-normality based on its canonical form," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 19-32.
  22. Bingtao Zhang & Peng Cao, 2019. "Classification of high dimensional biomedical data based on feature selection using redundant removal," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
  23. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
  24. Michel H. Montoril & Woojin Chang & Brani Vidakovic, 2019. "Wavelet-Based Estimation of Generalized Discriminant Functions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 318-349, December.
  25. Yaqiong Cui & Jukka Sirén & Timo Koski & Jukka Corander, 2016. "Simultaneous Predictive Gaussian Classifiers," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 73-102, April.
  26. Giovanni Felici & Klaus Truemper, 2002. "A MINSAT Approach for Learning in Logic Domains," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 20-36, February.
  27. Alejandro Murua & Nicolas Wicker, 2015. "Kernel-based mixture models for classification," Computational Statistics, Springer, vol. 30(2), pages 317-344, June.
  28. David González-Patiño & Yenny Villuendas-Rey & Magdalena Saldaña-Pérez & Amadeo-José Argüelles-Cruz, 2023. "A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification," IJERPH, MDPI, vol. 20(4), pages 1-13, February.
  29. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.
  30. West, David & Mangiameli, Paul & Rampal, Rohit & West, Vivian, 2005. "Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application," European Journal of Operational Research, Elsevier, vol. 162(2), pages 532-551, April.
  31. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
  32. Ryu, Young U. & Chandrasekaran, R. & Jacob, Varghese S., 2007. "Breast cancer prediction using the isotonic separation technique," European Journal of Operational Research, Elsevier, vol. 181(2), pages 842-854, September.
  33. Ramazan Ünlü & Petros Xanthopoulos, 2019. "A weighted framework for unsupervised ensemble learning based on internal quality measures," Annals of Operations Research, Springer, vol. 276(1), pages 229-247, May.
  34. R. Chandrasekaran & Young U. Ryu & Varghese S. Jacob & Sungchul Hong, 2005. "Isotonic Separation," INFORMS Journal on Computing, INFORMS, vol. 17(4), pages 462-474, November.
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