Comparison of different classifiers with varying amounts of missing data.
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https://figshare.com/articles/dataset/Comparison_of_different_classifiers_with_varying_amounts_of_missing_data_/9972350
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This table compares the performance of different classifiers for the original MIMIC II data and a version of the MIMIC II data where 50% of observations were replaced randomly with missing values. We see that N-UFA is robust to missing data, with accuracy decreasing just 1.3% as the amount of missing data increases to 50%. An expanded version of Table 6 including confidence intervals and results for 5–25% missing data is available in the S1 Table.
创建时间：
2019-10-11
