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An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder
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DOI：10.1109/ACCESS.2018.2868729.png)
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摘要
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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.
Keyword:
Imputation
extreme learning machine
auto-encoder
generalized mean absolute deviation
purity
K-means clustering
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IF:
3.6
论文数:
9.8W
被引数:
29.4W
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