Abstract
The process of combining an ensemble of classifiers has been deemed to be an efficient way for improving the performance of several classification problems. The Random Subspace Method, that consists of training a set of classifiers on different subsets of the feature space, has been shown to be effective in increasing the accuracy of classifiers, notably the nearest neighbor one. Since, in several real world domains, data can also be suffered from several aspects of uncertainty, including incompleteness and inconsistency, an Enhanced Evidential k-Nearest Neighbor classifier has been recently introduced to deal with the uncertainty pervading both the attribute values and the classifier outputs within the belief function framework. Thus, in this paper, we are based primarily on the Enhanced Evidential k-Nearest Neighbor classifier to construct an ensemble pattern classification system. More precisely, we adopt the Random Subspace Method in our context to build ensemble classifiers with imperfect data.
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Trabelsi, A., Elouedi, Z., Lefevre, E. (2017). Ensemble Enhanced Evidential k-NN Classifier Through Random Subspaces. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_20
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