A robust learning model for dealing with missing values in many-core architectures
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Naïve bayes vs. support vector machine: resilience to missing data
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Noisy data elimination using mutual k-nearest neighbor for classification mining
Journal of Systems and Software
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
WIMP: Web server tool for missing data imputation
Computer Methods and Programs in Biomedicine
Classifying patterns with missing values using Multi-Task Learning perceptrons
Expert Systems with Applications: An International Journal
Boosting with side information
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A new hybrid metaheuristic for medical data classification
International Journal of Metaheuristics
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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.