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- W2891713324 abstract "This paper proposes an imputation method for missing data based on an extreme learning machine auto-encoder (ELM-AE). The imputation chooses a set of plausible values determined by ELM-AE and then substitutes the average of these plausible values for the missing values. To compare the performance of ELM-AE imputation with the three other widely used imputation techniques, we conducted comprehensive experiments using seven UCI benchmark data sets. The proposed ELM-AE imputation approach proved to be superior to the other three methods based on the results using these data sets." @default.
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- W2891713324 date "2018-01-01" @default.
- W2891713324 modified "2023-10-16" @default.
- W2891713324 title "An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder" @default.
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- W2891713324 doi "https://doi.org/10.1109/access.2018.2868729" @default.
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