One-class kernel subspace ensemble for medical image classification

Eurasip Journal on Advances in Signal Processing

Volumn 2014, Issue 1, 2014, Pages

  Zhang, Yungang ^a,c   Zhang, Bailing ^c   Coenen, Frans ^b   Xiao, Jimin ^c   Lu, Wenjin ^c  

^a YUNNAN NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c XI AN JIAOTONG LIVERPOOL UNIVERSITY   (China)

Author keywords

Biopsy image; Breast cancer diagnosis; Classifier ensemble; Kernel principle component analysis; One class classifier

Indexed keywords

BIOPSY; CLASSIFICATION (OF INFORMATION); DISEASES; MEDICAL IMAGING; OPTICAL TOMOGRAPHY; PRINCIPAL COMPONENT ANALYSIS;

BIOPSY IMAGES; BREAST CANCER DIAGNOSIS; CLASSIFIER ENSEMBLES; KERNEL PRINCIPLE COMPONENT ANALYSIS; ONE-CLASS CLASSIFIER;

IMAGE CLASSIFICATION;

EID: 84894693790     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1186/1687-6180-2014-17     Document Type: Article

References (59)

1. Boucheron LE: Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer. Thesis, University of California Santa Barbara, 2008
2. Loukas C: A survey on histological image analysis-based assessment of three major biological factors influencing radiotherapy: proliferation, hypoxia and vasculature. Comput. Methods Programs Biomed 2004,74(3):183-199.
3. Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg IG: WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit. Lett 2008,29(11):1684-1693.
4. Kuncheva L, Rodriguez J, Plumpton C, Linden D, Johnston S: Random subspace ensembles for FMRI classification. IEEE Trans. Med. Imaging 2010,29(2):531-542.
5. Tax D: One-class classification. Thesis, Delft University of Technology, 2001
6. Rokach L: Ensemble-based classifiers. Artif. Intell. Rev 2010, 33:1-39.
7. Goh K-S, Chang EY, Li B: Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans. Knowl. Data Eng 2005,17(10):1333-1346.
8. Bergamini C, Oliveira L, Koerich A, Sabourin R: Combining different biometric traits with one-class classification. Signal Process 2009, 89:2117-2127.
9. Haghighi MS, Vahedian A, Yazdi HS: Creating and measuring diversity in multiple classifier systems using support vector data description. Appl. Soft Comput 2011, 11:4931-4942.
10. Bryll R, Guitierrez-Osuna R, Quek F: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognit 2003, 36:1291-1302.
11. Kuncheva L, Jain LC: Designing classifier fusion systems by genetic algorithms. IEEE Trans. Evol. Comput 2000,4(4):327-336.
12. Zhang L, Zhang L: On combining multiple features for hyperspectral remote sensing image classification. IEEE Trans. Geoscience Remote Sensing 2012,50(3):879-893.
13. Yu J, Lin F, Seah H-S, Li C, Lin Z: Image classification by multimodal subspace learning. Pattern Recognit. Lett 2012, 33:1196-1204.
14. Moya M, Koch M, Hostetler L: One-class classifier networks for target recognition applications. In Proceedings of World Congress on Neural Networks. Portland; July 1993:797-801.
15. Khan SS, Madden MG: A survey of recent trends in one class classification. In Artificial Intelligence and Cognitive Science, Lecture Notes in Computer Science, vol. 6206. Edited by: Coyle L, Freyne J. Berlin, Heidelberg: Springer; 2010:188-197.
16. Markou M, Singh S: Novelty detection: a review-part 1: statistical approaches. Signal Process 2003, 83:2481-2497.
17. Markou M, Singh S: Novelty detection: a review-part 2: neural network based approaches. Signal Processing 2003, 83:2499-2521.
18. Tax DM, Duin RP: Support vector domain description. Pattern Recognit. Lett 1999, 20:1191-1199.
19. Tax DM, Duin RP: Support vector data description. Mach. Learn 2004, 54:45-66.
20. Schölkopf B, Platt J, Shawe-Taylor J, Smola A, Williamson RC: Estimating the support of a high dimensional distribution. Neural Comput 2001,13(7):1443-1472.
21. Manevitz LM, Yousef M: One-class SVMs for document classification. J. Mach. Learn. Res 2001, 2:139-154.
22. Lewis DD: Test collections - Reuters-21578. http://www.daviddlewis.com/ resources/testcollections/reuters21578. Accessed 22 June 2013
23. Roth V: Kernel fisher discriminants for outlier detection. Neural Comput 2006, 18:942-960.
24. Ridder D, Tax D, Duin D: An experimental comparison of one-class classification methods. In Proceedings of the 4th Annual Conference of the Advanced School for Computing and Imaging. Holland: Delft; 1998:213-218.
25. Wang Q, Lopes L, Tax D: Visual object recognition through one-class learning. In International Conference on Image Analysis and Recognition, Porto, Portugal. Springer, Berlin; 2004:463-470.
26. Beyer K, Goldstein J, Ramakrishnan R, Shaft U: When is 'nearest neighbor' meaningful? Lect. Notes Comput. Sci 1999, 540:217-235.
27. JIT: Principal Component Analysis. New York: Springer; 1986.
28. Zhang H, Huang W, Huang Z, Zhang B: A kernel autoassociator approach to patter classification. IEEE Trans. Syst., Man Cybernetics-Part B: Cybern 2005,35(3):593-606.
29. Hoffmann H: Kernel PCA for novelty detection. Pattern Recognit 2007, 40:863-874.
30. Tax DM, Duin RP: Combining one-class classifiers. In Proceedings of Multiple Classifier Systems. Berlin: Springer; 2001:299-308.
31. Shieh AD, Kamm DF: Ensembles of one class support vector machines. In Proceedings of the Multiple Classifier Systems. Berlin: Springer; 2009:181-190.
32. Jain AK, Duin RPW, Mao J: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell 2000,22(1):4-37.
33. Perdisci R, Gu G: Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2006). Piscataway: IEEE Computer Society; 2006:488-498.
34. Krawczyk B: Diversity in ensembles for one-class classification. In Advances in Intelligent Systems and Computing, New trends in databases and information systems, vol. 185. Edited by: Pechenizkiy M, Wojciechowski M. Heidelberg: Springer, Berlin; 2013:119-129.
35. Yang P, Yang YH, Zhou BB, Zomaya AY: A review of ensemble methods in bioinformatics. Curr. Bioinformatics 2010,5(4):296-308.
36. Li P, Chan KL, Krishnan SM: Learning a multi-size patch-based hybrid kernel machine ensemble for abnormal region detection in colonoscopic images. In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR 2005). Piscataway: IEEE Computer Society; 2005:670-675.
37. Li P, Chan KL, Fu S, Krishnan SM: An abnormal ecg beat detector approach for long-term monitoring of heart patients based on hybrid kernel machine ensemble. In Proceedings of the International Workshop on Multiple Classifier Systems (MCS 2005). Heidelberg: Springer; 2005:346-355.
38. Okun O, Priisalu H: Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors. Artif. Intell. Med 2009, 45:151-162.
39. Schölkopf B: The kernel trick for distances. Technical report MSR-TR-2000-51, Microsoft Research, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052 (2000)
40. Kallas M, Honeine P, Richard C, Francis C, Amoud H: Non-negativity constraints on the pre-image for pattern recognition with kernel machines. Pattern Recognit 2013, 46:3066-3080.
41. Mika S, Schölkopf B, Smola A, Müller K-R, Scholz M, Rätsch G: Kernel PCA and de-noising in feature spaces. In Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. Cambridge: MIT Press; 1998:536-542.
42. Kwok JT-Y, Tsang IW-H: The pre-image problem in kernel methods. IEEE Trans. Neural Netw 2004,15(6):1517-1525.
43. Zheng W-S, Lai J, Yuen PC: Penalized preimage learning in kernel principle component analysis. IEEE Trans. Neural Netw 2010,21(4):551-570.
44. Williams C: On a connection between kernel PCA and metric multidimensional scaling. In Advances in Neural Information Processing Systems 13, NIPS 2001. Cambridge: MIT Press; 2001:675-681.
45. Kitten J, Hate M, Duin RP, Matas J: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell 1998,20(3):226-239.
46. Kuncheva LI: Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley; 2004.
47. Breast cancer data. http://ftp.cs.technion.ac.il/pub/projects/medic- image. Accessed 22 June 2013
48. UCI: Machine learning repository. http://archive.ics.uci.edu/ml/datasets/ . Accessed 22 June 2013
49. Guo Z, Zhang L, Zhang D: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process 2010,19(6):1657-1663.
50. Haralick R, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans. Syst., Man Cybern 1973,3(6):610-621.
51. Candes E, Demanet L, Donoho D, Ying L: Fast discrete curvelet transforms. Multiscale Model. Simul 2006, 5:861-899.
52. Zhang Y, Zhang B, Coenen F, Lu W: Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach. Vis. Appl 2012, 1-17. doi:10.1007/s00138-012-0459-8
53. Albarrak A, Coenen F, Zheng Y: Age-related macular degeneration identification in volumetric optical coherence tomography using decomposition and local feature extraction. In Proceedings of 2013 International Conference on Medical Image, Understanding and Analysis. University of Birmingham; 17-19 July 2013:59-64.
54. Brook A, El-Yaniv R, Isler E, Kimmel R, Meir R, Peleg D: Breast cancer diagnosis from biopsy images using generic features and SVMs. Technical report CS-2008-07, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Isreal (2006)
55. Doyle S, Feldman MD, Shih N, Tomaszewki J, Madabhushi A: Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinformatics 2012,13(282):1-15.
56. Krishnan MMR, Venkatraghavan V, Acharya UR, Pal M, Paul RR, Min LC, Ray AK, Chatterjee J, Chakraborty C: Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. BMC Bioinformatics 2012,13(282):1-15.
57. Valdovinos R, Sanchez J: Performance analysis of classifier ensembles: neural networks versus nearest neighbor rule. Pattern Recognit Image Anal. (Lecture Notes in Computer Science) 2007, 4477:105-112.
58. Gou S, Yang H, Jiao L, Zhuang X: Algorithm of partition based network boosting for imbalanced data classification. In Proceedings of the 2010 International Joint Conference on Neural Networks, IJCNN'10. Piscataway: IEEE; 2010:1-6.
59. Luo S, Cheng B: Diagnosing breast masses in digital mammography using feature selection and ensemble methods. J. Med. Syst 2012,36(2):569-577.