Effect of separate sampling on classification accuracy

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Type: Journal Publication

Web: http://bioinformatics.oxfordjournals.org/content/30/2/242


Abstract: Motivation: Measurements are commonly taken from two phenotypes to build a classifier, where the number of data points from each class is predetermined, not random. In this ‘separate sampling’ scenario, the data cannot be used to estimate the class prior probabilities. Moreover, predetermined class sizes can severely degrade classifier performance, even for large samples. Results: We employ simulations using both synthetic and real data to show the detrimental effect of separate sampling on a variety of classification rules. We establish propositions related to the effect on the expected classifier error owing to a sampling ratio different from the population class ratio. From these we derive a sample-based minimax sampling ratio and provide an algorithm for approximating it from the data. We also extend to arbitrary distributions the classical population-based Anderson linear discriminant analysis minimax sampling ratio derived from the discriminant form of the Bayes classifier.


Cited as: Mohammad Shahrokh Esfahani and Edward R. Dougherty, "Effect of separate sampling on classification accuracy", Bioinformatics (2014) 30 (2): 242-250.

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