Statistical analysis with missing data
Statistical analysis with missing data
Training Algorithm with Incomplete Data for Feed-ForwardNeural Networks
Neural Processing Letters
Local distance-based classification
Knowledge-Based Systems
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
A SVM regression based approach to filling in missing values
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Removal and interpolation of missing values using wavelet neural network for heterogeneous data sets
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Classifying patterns with missing values using Multi-Task Learning perceptrons
Expert Systems with Applications: An International Journal
A data mining driven risk profiling method for road asset management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting mistakes in binary data tables
Automatic Documentation and Mathematical Linguistics
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Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. These data set sizes range from 47 to 1389 records. A perturbation experiment was performed for each data set where the probability of missing value was set to 0.05. Several architectures and learning algorithms for the multilayer perceptron are tested and compared with three classic imputation procedures: mean/mode imputation, regression and hot-deck. The obtained results, considering different performance measures, not only suggest this approach improves the quality of a database with missing values, but also the best results are clearly obtained using the Multilayer Perceptron model in data sets with categorical variables. Three learning rules (Levenberg-Marquardt, BFGS Quasi-Newton and Conjugate Gradient Fletcher-Reeves Update) and a small number of hidden nodes are recommended.