Abstract
This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Shafer theory. In order to properly estimate the class labels, different types of features are extracted from the data. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern, in each of the feature spaces, is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptrons neural network is used as base classifier and for a given test sample, its outputs are considered as basic belief assignment. Finally, the decisions of the base classifiers are combined using Dempster’s rule of combination. Experiments with artificial and real data demonstrate that considering ambiguity in class labels can provide better results than classifiers trained with imperfect labels.
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References
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Smets, P., Kennes, R.: The Transferable Belief Model. Artif. Intell. 66, 191–243 (1994)
Rogova, G.: Combining the result of several neural network classifiers. Neural Networks 7(5), 777–781 (1994)
Denoeux, T.: A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. IEEE Trans. Syst., Man, Cybern. 25(3), 804–813 (1995)
Denoeux, T.: A Neural Network Classifier Based on Dempster-Shafer Theory. IEEE Trans. Syst., Man, Cybern. A, Syst., Humans 30, 131–150 (2000)
Basir, O., Karray, F., Zhu, H.: Connectionist-Based Dempster-Shafer Evidential Reasoning for Data Fusion. IEEE Trans. on Neural Net. 16, 1513–1529 (2005)
Quost, B., Denoeux, T., Masson, M.-H.: Pairwise classifier combination using belief functions. Pattern Recognition Letters 28, 644–653 (2007)
Smets, P.: Belif Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)
Elouedi, Z., Mellouli, K., Smets, P.: Assessing Sensor Reliability for Multisensor Data Fusion Within the Transferable Belief Model. IEEE Trans. Syst., Man, Cybern. B 34, 782–787 (2004)
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Tabassian, M., Ghaderi, R., Ebrahimpour, R. (2010). Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_20
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DOI: https://doi.org/10.1007/978-3-642-16773-7_20
Publisher Name: Springer, Berlin, Heidelberg
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