EM-Based Radial Basis Function Training with Partial Information
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Feature Selection for Ensembles of Simple Bayesian Classifiers
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Flexible neural trees ensemble for stock index modeling
Neurocomputing
A novel ensemble of classifiers for microarray data classification
Applied Soft Computing
Improving classification performance on real data through imputation
AQTR '08 Proceedings of the 2008 IEEE International Conference on Automation, Quality and Testing, Robotics - Volume 03
Combining Multiple Classifiers with Dynamic Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Feature subset selection in large dimensionality domains
Pattern Recognition
Estimation of Missing Values Using a Weighted K-Nearest Neighbors Algorithm
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03
Online adaptive policies for ensemble classifiers
Neurocomputing
A general regression neural network
IEEE Transactions on Neural Networks
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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The treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach-GESI. We compare our algorithms with 25 popular missing data imputation algorithms on 98 real-world and synthetic datasets for various percentage of missing values. The effectiveness of the algorithms is evaluated in terms of (i) the accuracy of output classification: three classifiers (a generalized regression neural network, a multilayer perceptron and a logistic regression technique) are separately trained and tested on the dataset imputed with each imputation algorithm, (ii) interval analysis with missing observations and (iii) point estimation accuracy of the missing value imputation. GEMI outperformed GESI and all the conventional imputation algorithms in terms of all three criteria considered.