Missing Value Imputation with Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real‐world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to h...

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Veröffentlicht in:	Computational intelligence 2016-05, Vol.32 (2), p.196-215
Hauptverfasser:	Gashler, Michael S., Smith, Michael R., Morris, Richard, Martinez, Tony
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Back propagation Classification Data analysis Data mining Datasets Filtering imputation Learning manifold learning missing values neural networks Studies unsupervised learning
Online-Zugang:	Volltext
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