PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

New Method for Instance or Prototype Selection using Mutual Information in Time Series Prediction
Alberto Guillen, Luis Herrera, Gines Rubio, Hector Pomares, Amaury Lendasse and Ignacio Rojas
Neurocomputing 2010.

Abstract

The task of selecting an adequate subset of input vectors that are included in a training set when classifying, approximating or predicting an output is a relevant task that, if accomplished correctly, can provide storage and computational savings and im- prove the accuracy of the results. This problem can be tackled from different perspectives: outlier detection or instance selection of noise-free data. The concept of outlier was firstly introduced by Grubbs in [1] as: ”...an outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs...”. This concept was later extended by Barnett and Lewis [2] as: ”... An observation (or subset of ob- servations) which appears to be inconsistent with the remainder of that set of data...”. Many other notations (novelty detection, anomaly detection, noise detection, deviation detection, excep- tion mining) have been used for this problem [3].

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6656
Deposited By:Amaury Lendasse
Deposited On:08 March 2010