Pattern classification with missing data: a review

• Published in: Neural computing & applications Vol. 19; no. 2; pp. 263 - 282
• Main Authors: García-Laencina, Pedro J., Sancho-Gómez, José-Luis, Figueiras-Vidal, Aníbal R.
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.03.2010; Springer
• Subjects: Applications; Applied sciences; Artificial Intelligence; Biology, psychology, social sciences; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Learning and adaptive systems; Mathematics; Medical sciences; Multivariate analysis; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics; Missing data; Neural networks; Pattern classification; Machine learning; Biometrics; Statistical method; Neural computation; Solving; Statistical theory; Review; Diagnosis; Pattern recognition; Neural network; Application
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-009-0295-6

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.