HANDLING MISSING VALUES VIA A NEURAL SELECTIVE INPUT MODEL
نویسندگان
چکیده
منابع مشابه
Handling Missing Values via a Neural Selective Input Model
Missing data represent an ubiquitous problem with numerous and diverse causes. Handling Missing Values (MVs) properly is a crucial issue, in particular in Machine Learning (ML) and pattern recognition. To date, the only option available for standard Neural Networks (NNs) to handle this problem has been to rely on pre-processing techniques such as imputation for estimating the missing data value...
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Missing Values and its problems are very common in the data cleaning process. Several methods have been proposed so as to process missing data in datasets and avoid problems caused by it. This paper discusses various problems caused by missing values and different ways in which one can deal with them. Missing data is a familiar and unavoidable problem in large datasets and is widely discussed i...
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ژورنال
عنوان ژورنال: Neural Network World
سال: 2012
ISSN: 1210-0552,2336-4335
DOI: 10.14311/nnw.2012.22.021