{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/feature-selection-in-omics-prediction","title":"Feature selection in omics prediction problems using cat scores and false nondiscovery rate control","arxiv_id":"0903.2003","date":"2009-03-11","proceeding":null,"authors":["Miika Ahdesmäki","Korbinian Strimmer"],"abstract":"We revisit the problem of feature selection in linear discriminant analysis\n(LDA), that is, when features are correlated. First, we introduce a pooled\ncentroids formulation of the multiclass LDA predictor function, in which the\nrelative weights of Mahalanobis-transformed predictors are given by\ncorrelation-adjusted $t$-scores (cat scores). Second, for feature selection we\npropose thresholding cat scores by controlling false nondiscovery rates (FNDR).\nThird, training of the classifier is based on James--Stein shrinkage estimates\nof correlations and variances, where regularization parameters are chosen\nanalytically without resampling. Overall, this results in an effective and\ncomputationally inexpensive framework for high-dimensional prediction with\nnatural feature selection. The proposed shrinkage discriminant procedures are\nimplemented in the R package ``sda'' available from the R repository CRAN.","url_abs":"http://arxiv.org/abs/0903.2003v4","url_pdf":"http://arxiv.org/pdf/0903.2003v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"feature-selection-in-omics-prediction","repo_url":"https://github.com/mjafin/shrinkage_da","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}