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- W2950562571 abstract "It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble classifiers. Experimental results show that DMT is significantly more accurate than other benchmark ensemble classifiers on noisy test data. We also discuss a limitation of DMT and its possible variations." @default.
- W2950562571 created "2019-06-27" @default.
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- W2950562571 date "2016-12-18" @default.
- W2950562571 modified "2023-09-24" @default.
- W2950562571 title "Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data" @default.
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- W2950562571 doi "https://doi.org/10.48550/arxiv.1612.05888" @default.
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