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Neural Computing and Applications

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01.03.2010 | Original Article

Pattern classification with missing data: a review

verfasst von: Pedro J. García-Laencina, José-Luis Sancho-Gómez, Aníbal R. Figueiras-Vidal

Erschienen in: Neural Computing and Applications | Ausgabe 2/2010

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Abstract

Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

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Titel: Pattern classification with missing data: a review
verfasst von: Pedro J. García-Laencina
José-Luis Sancho-Gómez
Aníbal R. Figueiras-Vidal
Publikationsdatum: 01.03.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0295-6

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