PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Instance or Prototype Selection for Function Approximation using Mutual Information
Alberto Guillén, Luis Herrera, Gines Rubio Rubio, Amaury Lendasse, Hector Pomares and Ignacio Rojas
In: European Symposium on Time Series Prediction (ESTSP'08), 17 - 19 September 2008, Porvoo, Finland.

Abstract

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. The computation of the mutual information for regression tasks has been recently proposed providing good results in feature selection. 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 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 distinguish this work from others is that it is not proposed as an outlier identificator but as algorithm that determine 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 a highly distorted data sets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4794
Deposited By:Amaury Lendasse
Deposited On:24 March 2009