Signal-to-noise statistic

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In mathematics the signal-to-noise statistic distance between two vectors a and b with mean values \mu _a and \mu _b and standard deviation \sigma _a and \sigma _b respectively is:

D_{sn} = {(\mu _a - \mu _b) \over (\sigma _a + \sigma _b)}

In the case of Gaussian-distributed data and unbiased class distributions, this statistic can be related to classification accuracy given an ideal linear discrimination, and a decision boundary can be derived.[1]

This distance is frequently used to identify vectors that have significant difference. One usage is in bioinformatics to locate genes that are differential expressed on microarray experiments.[2]

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Notes[edit]

  1. ^ Auffarth, B., Lopez, M., Cerquides, J. (2010). Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images. Advances in Data Mining. Applications and Theoretical Aspects. p. 248--262. Springer. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.1528
  2. ^ Pomeroy, S.L. et al. Gene Expression-Based Classification and Outcome Prediction of Central Nervous System Embryonal Tumors. Nature 415, 436–442.