Noise Sensitivity Signatures for Model Selection

Tal Grossman and Alan Lapedes

Working Papers from Santa Fe Institute

Abstract: We present a method for calculating the ``noise sensitivity signature'' of a learning algorithm which is based on scrambling the output of classes of various fractions of the training data. This signature can be used to indicate a good (or bad) match between the complexity of the classifier and the complexity of the data and hence to improve the predictive accuracy of a classification algorithm. Use of noise sensitivity signatures is distinctly different from other schemes to avoid overtraining, such as cross-validation, which uses only part of the training data, or various penalty functions, which are not data-adaptive. Noise sensitivity signature methods use all of the training data and are manifestly data-adaptive and non-parametric. They are well suited for situations with limited training data.

Date: 1995-02
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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:95-02-018

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