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Random Prism: a noise-tolerant alternative to Random Forests

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Figure

Fig. 1 Cendrowska’s replicated subtree example.
Fig. 2 The Random Prism architecture comprising Bagging, R-PrismTCS base clas- clas-sifiers and weighted majority voting.
Table 1 Example data for weighted majority voting Classifier Weight A 0.55 B 0.65 C 0.55 D 0.95 E 0.85
Table 2 Accuracy of Random Prism compared with PrismTCS [24].
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