Imputation of Missing Data in Industrial Databases
Applied Intelligence
A pseudo-nearest-neighbor approach for missing data recovery on Gaussian random data sets
Pattern Recognition Letters
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
IEEE Transactions on Knowledge and Data Engineering
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
EROS: Ensemble rough subspaces
Pattern Recognition
Content-based image database system for epilepsy
Computer Methods and Programs in Biomedicine
Impact of missing data in evaluating artificial neural networks trained on complete data
Computers in Biology and Medicine
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
A unifying view on dataset shift in classification
Pattern Recognition
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Dynamic discriminant functions with missing feature values
Pattern Recognition Letters
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This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values.