Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Intelligent Selection of Instances for Prediction Functions in LazyLearning Algorithms
Artificial Intelligence Review - Special issue on lazy learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Stopping criterion for boosting based data reduction techniques: from binary to multiclass problem
The Journal of Machine Learning Research
Genetic algorithms for outlier detection and variable selection in linear regression models
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Studying possibility in a clustering algorithm for RBFNN design for function approximation
Neural Computing and Applications
Efficient Parallel Feature Selection for Steganography Problems
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Non-parametric residual variance estimation in supervised learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Effective input variable selection for function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Editorial: European Symposium on Times Series Prediction
Neurocomputing
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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The problem of selecting the patterns to be learned by any model is usually not considered by the time of designing the concrete model but as a preprocessing step. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Recently the computation of the mutual information for regression tasks has been proposed so this paper presents a new application of the concept of mutual information not to select the variables but to decide which prototypes should belong to the training data set in regression problems. The proposed methodology consists in deciding if a prototype should belong to or not to the training set using as criteria the estimation of the mutual information between the variables. The novelty of the approach is to focus in prototype selection for regression problems instead of classification as the majority of the literature deals only with the last one. Other element that distinguishes this work from others is that it is not proposed as an outlier detector but as an algorithm that determines the best subset of input vectors by the time of building a model to approximate it. As the experiment section shows, this new method is able to identify a high percentage of the real data set when it is applied to highly distorted data sets.