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- W2128192362 abstract "Missing values are a problem occuring in the analysis of most application datasets. Though the deletion of observations with missing values is common practice, the loss of information in doing so is regrettable. This is even more the case for high-dimensional data, where the structural information in many components of an observation is lost because of few missing values. A new method is suggested for missing value imputation. The basis of the presented approach is an emergent self-organizing neural network that adjusts to the inherent structure of the input data. The technique has been successfully tested on a real-world example of financial data and turned out to be superior to the other methods under consideration." @default.
- W2128192362 created "2016-06-24" @default.
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- W2128192362 date "2002-01-01" @default.
- W2128192362 modified "2023-10-11" @default.
- W2128192362 title "The Completion of Missing Values by Neural Nets for Data Mining" @default.
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- W2128192362 doi "https://doi.org/10.1007/978-3-642-55991-4_24" @default.
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