References
- R. Akbani, S. Kwek, and N. Japkpwicz, Applying support vector machines to imbalanced datasets, Lect. Notes Comput. Sci. 3201 (2004), pp. 39–50. doi: 10.1007/978-3-540-30115-8_7
- J.F. Aujol and A. Chambolle, Dual norms and image decomposition models, Int. J. Comput. Vis. 63 (2005), pp. 85–104. doi: 10.1007/s11263-005-4948-3
- F. Chabat, G.Z. Yang, and D.M. Hansell, Obstructive lung diseases: Texture classification for differentiation at CT, Radiology 228 (2003), pp. 871–877. doi: 10.1148/radiol.2283020505
- A. Chambolle, An algorithm for total variation minimization and applications, J. Math. Imaging Vis. 20 (2004), pp. 89–97. doi: 10.1023/B:JMIV.0000011320.81911.38
- N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, SMOTE: Synthetic Minority Over-sampling Technique, J. Artif. Intell. Res. 16 (2002), pp. 321–357.
- S. Ertekin, J. Huang, L. Bottou, and L. Giles, Learning on the border: Active learning in imbalanced data classification, Proceeding of the ACM Conference on Information and Knowledge Management (CIKM 07), Lisboa Portugal, 2007.
- B. Han, M. Yu, and D. McEntegart, Weighted re-randomization tests for minimization with unbalanced allocation, Pharm. Stat. 12(4) (2013), pp. 243–253. doi: 10.1002/pst.1577
- R.M. Haralick and K. Shanmugam, Textural features for image classification, IEEE Trans. Syst. Man Cybern. 3 (1973), pp. 610–621. doi: 10.1109/TSMC.1973.4309314
- H. He and E.A. Garcia, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng. 21 (2009), pp. 1263–1284. doi: 10.1109/TKDE.2008.239
- N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study, Intell. Data Anal. 6(5) (2002), pp. 429–449.
- H.J. Kim, G. Li, D. Gjertson, R. Elashoff, S.K. Shah, R. Ochs, F. Vasunilashorn, F. Abtin, M.S. Brown, and J.G. Goldin, Classification of parenchymal abnormality in scleroderma lung using a novel approach to denoise images collected via a multicenter study, Acad. Radiol. 15 (2008), pp. 1004–1016. doi: 10.1016/j.acra.2008.03.011
- P. Mitra, C.A. Murthy, and S.K. Pal, A probabilistic active support vector learning algorithm, IEEE Trans. Pattern Anal. Mach. Intell. 26(3) (2004), pp. 413–418. doi: 10.1109/TPAMI.2004.1262340
- E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W.J. Emery, SVM active learning approach for image classification using spatial information, IEEE Trans. Geosci. Remote Sens. 52(4) (2014), pp. 2217–2233. doi: 10.1109/TGRS.2013.2258676
- G. Schohn and D. Cohn, Less is more: Active learning with support vector machines, International Workshop on Machine Learning (ICML), Stanford, CA. 2000.
- A.J. Smola and B. Schölkopf, Sparse greedy matrix approximation for machine learning, International Workshop on Machine Learning (ICML), Stanford, CA, 2000.
- M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.
- S. Tong and D. Koller, Support vector machine active learning with applications to text classification, J. Mach. Learn. Res. 2 (2002), pp. 45–66.
- V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
- T. Watadani, F. Sakai, T. Johkoh, S. Noma, M. Akira, K. Fujimoto, A.A. Bankier, K.S. Lee, N.L. Müller, J.-W. Song, J.-S. Park, D.A. Lynch, D.M. Hansell, M. Remy-Jardin, T. Franquet, and Y. Sugiyama, Interobserver variability in the CT assessment of honeycombing in the lungs, Radiology 266(3) (2013), pp. 936–944. doi: 10.1148/radiol.12112516