Effect of separate sampling on classification accuracy | Zendy

Mohammad Shahrokh Esfahani | Zendy; Edward R. Dougherty | Zendy

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Effect of separate sampling on classification accuracy

Author(s) -

Mohammad Shahrokh Esfahani,

Edward R. Dougherty

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt662

Subject(s) - minimax , classifier (uml) , computer science , matlab , sample size determination , naive bayes classifier , bayes' theorem , sampling (signal processing) , population , bayes classifier , statistics , bayes error rate , artificial intelligence , data mining , pattern recognition (psychology) , mathematics , bayesian probability , mathematical optimization , support vector machine , sociology , demography , filter (signal processing) , computer vision , operating system

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.

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