The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Algorithm with Incomplete Data for Feed-ForwardNeural Networks
Neural Processing Letters
Robust Learning with Missing Data
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging and the Random Subspace Method for Redundant Feature Spaces
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Hybrid Neural Network System for Pattern Classification Tasks with Missing Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Moderate diversity for better cluster ensembles
Information Fusion
Imputation through finite Gaussian mixture models
Computational Statistics & Data Analysis
On Classification with Incomplete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical likelihood confidence intervals for differences between two datasets with missing data
Pattern Recognition Letters
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
IEEE Transactions on Neural Networks
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Random feature subset selection for ensemble based classification of data with missing features
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
A Multi-Expert System for chlorine electrolyzer monitoring
Expert Systems with Applications: An International Journal
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We introduce Learn^+^+.MF, an ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature problem in supervised classification. Unlike most established approaches, Learn^+^+.MF does not replace missing values with estimated ones, and hence does not need specific assumptions on the underlying data distribution. Instead, it trains an ensemble of classifiers, each on a random subset of the available features. Instances with missing values are classified by the majority voting of those classifiers whose training data did not include the missing features. We show that Learn^+^+.MF can accommodate substantial amount of missing data, and with only gradual decline in performance as the amount of missing data increases. We also analyze the effect of the cardinality of the random feature subsets, and the ensemble size on algorithm performance. Finally, we discuss the conditions under which the proposed approach is most effective.