{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T02:59:28Z","timestamp":1778295568393,"version":"3.51.4"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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 \u2018separate sampling\u2019 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability: All the codes for synthetic data and real data examples are written in MATLAB. A function called mmratio, whose output is an approximation of the minimax sampling ratio of a given dataset, is also written in MATLAB. All the codes are available at: http:\/\/gsp.tamu.edu\/Publications\/supplementary\/shahrokh13b.<\/jats:p>\n               <jats:p>Contact: \u00a0edward@ece.tamu.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt662","type":"journal-article","created":{"date-parts":[[2013,11,21]],"date-time":"2013-11-21T01:25:33Z","timestamp":1384997133000},"page":"242-250","source":"Crossref","is-referenced-by-count":79,"title":["Effect of separate sampling on classification accuracy"],"prefix":"10.1093","volume":"30","author":[{"given":"Mohammad","family":"Shahrokh Esfahani","sequence":"first","affiliation":[{"name":"1 Department of Electrical and Computer Engineering and 2Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA"},{"name":"1 Department of Electrical and Computer Engineering and 2Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward R.","family":"Dougherty","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering and 2Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA"},{"name":"1 Department of Electrical and Computer Engineering and 2Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,11,20]]},"reference":[{"key":"2023012710401561300_btt662-B1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/BF02313425","article-title":"Classification by multivariate analysis","volume":"16","author":"Anderson","year":"1951","journal-title":"Psychometrika"},{"key":"2023012710401561300_btt662-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Transact. 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