Abstract
Polychotomies are recognition tasks with a number of categories greater than two, consisting in assigning patterns to a finite set of classes. Although many of the learning algorithms developed so far are capable of handling polychotomies, most of them were designed by nature for dichotomies, that is, for binary learning. Therefore, various methods that decompose the multiclass recognition task in a set of binary learning problems have been proposed in the literature. After addressing the different dichotomies, the final decision is reconstructed according to a given criterion. Among the decomposition approaches, one of them is based on a pool of binary modules, where each one distinguishes the elements of one class from those of the others. For this reason, it is also known as one-per-class method. Under this decomposition scheme, we propose a novel reconstruction criterion to set the final decision on the basis of the single binary classifications. It looks at the quality of the current input and, more specifically, it is a function of the reliability of each classification act provided by the binary modules. The approach has been tested on six biological and medical datasets (two private, four public) and the achieved performance has been compared with the one previously reported in the literature, showing that the method improves the accuracies achieved so far.
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References
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Mayoraz, E., Moreira, M.: On the decomposition of polychotomies into dichotomies. In: ICML 1997: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 219–226 (1997)
Jelonek, J., Stefanowski, J.: Experiments on solving multiclass learning problems by n 2 classifier. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 172–177. Springer, Heidelberg (1998)
Masulli, F., Valentini, G.: Comparing decomposition methods for classication. In: KES 2000, Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, pp. 788–791 (2000)
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2001)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2002)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: NIPS 1997: Proceedings of the 1997 conference on Advances in neural information & processing systems, vol. 10, pp. 507–513. MIT Press, Cambridge (1998)
Kuncheva, L.I.: Using diversity measures for generating error-correcting output codes in classifier ensembles. Pattern Recognition Letters 26, 83–90 (2005)
Stefanowski, J.: Multiple and hybrid classifiers, 174–188 (2001)
Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 405–410 (1997)
Kuncheva, L.I.: Switching between selection and fusion in combining classifiers: anexperiment. IEEE Transactions on Systems, Man and Cybernetics, Part B 32, 146–156 (2002)
Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 52–66. Springer, Heidelberg (2000)
De Stefano, C., Sansone, C., 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, 84–93 (2000)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34, 299–314 (2001)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions On Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: Reliability parameters to improve combination strategies in multi-expert systems. Pattern Analysis & Applications 2, 205–214 (1999)
Cordella, L.P., Sansone, C., Tortorella, F., Vento, M., De Stefano, C.: Neural networks classification reliability. In: Academic Press theme volumes on Neural Network Systems, Techniques and Applications, vol. 5, pp. 161–199. Academic Press, London (1998)
Kavanaugh, A., Tomar, R., Reveille, J., Solomon, D.H., 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, 71–81 (2000)
Rigon, A., Soda, P., Zennaro, D., Iannello, G., Afeltra, A.: Indirect immunofluorescence in autoimmune diseases: Assessment of digital images for diagnostic purpose. Cytometry B (Clinical Cytometry) 72, 472–477 (2007)
Soda, P., Iannello, G.: A multi-expert system to classify fluorescent intensity in antinuclear autoantibodies testing. In: Computer Based Medical Systems, pp. 219–224. IEEE Computer Society, Los Alamitos (2006)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Clark, P., Niblett, T.: Induction in noisy domains. In: Proc. of Progress in Machine Learning, pp. 11–30 (1987)
Cheung, N.: Machine learning techniques for medical analysis. Master’s thesis, University of Queensland (2001)
Pappa, G.L., Freitas, A.A., Kaestner, C.A.A.: Attribute selection with a multi-objective genetic algorithm, 280–290 (2002)
Horton, P., Nakai, K.: A probabilistic classification system for predicting the cellular localization sites of proteins 4, 109–115 (1996)
Windeatt, T., Ghaderi, R.: Binary labelling and decision-level fusion. Information Fusion 2(2), 103–112 (2001)
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Soda, P. (2008). Facing Polychotomies through Classification by Decomposition: Applications in the Bio-medical Domain. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2008. Communications in Computer and Information Science, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92219-3_22
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DOI: https://doi.org/10.1007/978-3-540-92219-3_22
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