On the use of classification reliability for improving performance of the one-per-class decomposition method

Data and Knowledge Engineering

Volumn 68, Issue 12, 2009, Pages 1398-1410

  Iannello, Giulio ^a   Percannella, Gennaro ^b   Sansone, Carlo ^c   Soda, Paolo ^a  

^a UNIVERSITY CAMPUS BIO MEDICO   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

^c UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

Binary classifiers; Classification reliability; Computer aided diagnosis systems; Decomposition method

Indexed keywords

BINARY CLASSIFIERS; CLASS DECOMPOSITION; CLASSIFICATION PERFORMANCE; CLASSIFICATION SYSTEM; COMPUTER-AIDED DIAGNOSIS SYSTEM; DECOMPOSITION METHODS; DECOMPOSITION SCHEME; IMPROVING PERFORMANCE; LARGE DATA; MEDICAL DATA SETS; MULTI-CLASS CLASSIFIER; MULTI-CLASS PROBLEMS; MULTICLASS CLASSIFICATION PROBLEMS; REAL APPLICATIONS; TYPICAL PATTERNS;

CLASSIFIERS; LEARNING SYSTEMS; OPERATIONS RESEARCH; PATTERN RECOGNITION; RELIABILITY; RISK PERCEPTION;

COMPUTER AIDED DIAGNOSIS;

EID: 71849097859     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.07.003     Document Type: Article

References (46)

1. Allwein E.L., Schapire R.E., and Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research 1 (2001) 113-141
2. A. Asuncion, D.J. Newman, UCI machine learning repository, 2007. URL .
3. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 7 (1997) 1145-1159
4. Cheng H.D., Cai X., Chen X., Hu L., and Lou X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36 12 (2003) 2967-2991
5. N. Cheung, Machine learning techniques for medical analysis, Master's Thesis, University of Queensland, 2001.
6. P. Clark, T. Niblett, Induction in noisy domains, in: Proceedings of the Progress in Machine Learning, 1987, pp. 11-30.
7. Cordella L.P., Foggia P., Sansone C., Tortorella F., and Vento M. Reliability parameters to improve combination strategies in multi-expert systems. Pattern Analysis and Applications 2 3 (1999) 205-214
8. L.P. Cordella, C. Sansone, F. Tortorella, M. Vento, C. De Stefano, Neural networks classification reliability, Academic Press Theme Volumes on Neural Network Systems, Techniques and Applications, vol. 5, Academic Press, 1998, pp. 161-199.
9. Crammer K., and Singer Y. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2 (2002) 265-292
10. De Santo M., Tortorella F., Molinara M., and Vento M. Automatic classification of clustered microcalcifications by a multiple expert system. Pattern Recognition 36 (2003) 1467-1477
11. De Stefano C., Sansone C., and Vento M. To reject or not to reject: that is the question: an answer in case of neural classifiers. IEEE Transactions on Systems, Man, and Cybernetics - Part C 30 1 (2000) 84-93
12. Dhawan A.P., Chitre Y., Kaiser-Bonasso C., and Moskowitz M. Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Transactions on Medical Imaging 15 3 (1996) 246-259
13. Dietterich T.G., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2 (1995) 263-286
14. T. Fawcett, ROC graphs: notes and practical considerations for data mining researchers, Tech. Rep., HP Laboratories Technical Report, 2003.
15. Foggia P., Percannella G., Sansone C., and Vento M. On rejecting unreliably classified patterns. Multiple Classifier Systems, Lecture Notes in Computer Science vol. 4472 (2007), Springer-Verlag, Heidelberg 282-291
16. Fumera G., and Roli F. Support vector machines with embedded reject option. Lecture Notes in Computer Science (2002) 68-82
17. Fürnkranz J. Round Robin classification. Journal of Machine Learning Research 2 (2002) 721-747
18. H. Hao, C.L. Liu, et al., Confidence evaluation for combining diverse classifiers, in: Proceedings of the 7th International Conference on Document Analysis and Recognition, 2003, pp. 760-765.
19. T. Hastie, R. Tibshirani, Classification by pairwise coupling, in: NIPS'97: Proceedings of the 1997 Conference on Advances in Neural Information and Processing Systems 10, MIT Press, Cambridge, MA, USA, 1998, pp. 507-513.
20. P. Horton, K. Nakai, A probabilistic classification system for predicting the cellular localization sites of proteins, in: Intelligent Systems in Molecular Biology, vol. 4, 1996, pp. 109-115.
21. Hsu C.-W., and Lin C.-J. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13 2 (2002) 415-425
22. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 1 (2000) 4-37
23. 2 classifier, in: 10th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998, pp. 172-177.
24. Kavanaugh A., Tomar R., Reveille J., Solomon D.H., and Homburger H.A. Guidelines for clinical use of the antinuclear antibody test and tests for specific autoantibodies to nuclear antigens. American College of Pathologists, Archives of Pathology and Laboratory Medicine 124 1 (2000) 71-81
25. Kumar S., Ghosh J., and Crawford M.M. Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis and Applications 5 2 (2002) 210-220
26. Kuncheva L.I. Combining Pattern Classifiers (2004), John Wiley & Sons, Inc., Hoboken, New Jersey, USA
27. Kuncheva L.I. Using diversity measures for generating error-correcting output codes in classifier ensembles. Pattern Recognition Letter 26 1 (2005) 83-90
28. F. Masulli, G. Valentini, Comparing decomposition methods for classification, in: KES'2000, Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000, pp. 788-791.
29. E. Mayoraz, M. Moreira, On the decomposition of polychotomies into dichotomies, in: Proceedings of the 14th International Conference on Machine Learning, 1997, pp. 219-226.
30. M. Moreira, E. Mayoraz, Improved pairwise coupling classification with correcting classifiers, in: 10th European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998, pp. 160-171.
31. S. Mukherjee, P. Tamayo, D. Slonim, A. Verri, T. Golub, J. Mesirov, T. Poggio, Support vector machine classification of microarray data. Tech. Rep., Massachusetts Institute of Technology, 1998.
32. Nattkemper T.W., Ritter H.J., and Schubert W. A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections. IEEE Transactions on Information Technology in Biomedicine 5 2 (2001) 138-149
33. G.L. Pappa, A.A. Freitas, C.A.A. Kaestner, Attribute selection with a multi-objective genetic algorithm, in: Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence, 2002, pp. 280-290.
34. Perner P., Perner H., and Muller B. Mining knowledge for HEp-2 cell image classification. Journal Artificial Intelligence in Medicine 26 (2002) 161-173
35. S. Rajan, J. Ghosh, An empirical comparison of hierarchical vs. two-level approaches to multiclass problems, in: Multiple Classifier Systems, 2004, pp. 283-292.
36. Rigon A., Soda P., Zennaro D., Iannello G., and Afeltra A. Indirect immunofluorescence in autoimmune diseases: assessment of digital images for diagnostic purpose. Cytometry B (Clinical Cytometry) 72 (2007) 472-477
37. P. Soda, Experiments on solving multiclass recognition tasks in the biological and medical domains, in: IEEE International Conference on Bio-inspired Systems and Signal Processing, 2008, pp. 64-71.
38. P. Soda, G. Iannello, A multi-expert system to classify fluorescent intensity in antinuclear autoantibodies testing, in: Computer Based Medical Systems, IEEE Computer Society, Los Alamitos, CA, USA, 2006, pp. 219-224.
39. P. Soda, G. Iannello, A hybrid multi expert systems for HEp-2 staining pattern classification, in: International Conference on Image Analysis and Processing, IEEE Computer Society, 2007, pp. 647-653.
40. Soda P., Iannello G., and Vento M. A multiple experts system for classifying fluorescence intensity in antinuclear autoantibodies analysis. Pattern Analysis and Applications (2008)
41. J. Stefanowski, Multiple and hybrid classifiers, in: Formal Methods and Intelligent Techniques in Control, Decision Making, Multimedia and Robotics, Post-Proceedings of 2nd Int. Conference, 2001, pp. 174-188.
42. Valentini G., and Dietterich T.G. Bias-variance analysis of support vector machines for the development of svm-based ensemble methods. Journal of Machine Learning Research 5 (2004) 725-775
43. Van Ginneken B., Ter Haar Romeny B.M., and Viergever M.A. Computer-aided diagnosis in chest radiography: a survey. IEEE Transactions on Medical Imaging 20 12 (2001) 1228-1241
44. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York,USA
45. Windeatt T., and Ghaderi R. Binary labelling and decision-level fusion. Information Fusion 2 2 (2001) 103-112
46. Würflinger T., Stockhausen J., Meyer-Ebrecht D., and Böcking A. Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images. Computerized Medical Imaging and Graphics 28 1-2 (2004) 87-98