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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.
AbstractThe 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]. [Edit]
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