{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:20:30Z","timestamp":1758270030319,"version":"3.41.2"},"reference-count":31,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2009,6,5]],"date-time":"2009-06-05T00:00:00Z","timestamp":1244160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,6,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to investigate the applicability of meta\u2010learning to the problem of algorithm recommendation for gene expression data classification.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>Meta\u2010learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: <jats:italic>k<\/jats:italic>\u2010nearest neighbors and support vector machine\u2010based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta\u2010learning literature.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>As the experiments conducted in this paper suggest, the use of meta\u2010learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>This paper reports contributions to the areas of meta\u2010learning and gene expression data analysis. Regarding the former, it supports the claim that meta\u2010learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563780910959901","type":"journal-article","created":{"date-parts":[[2009,6,6]],"date-time":"2009-06-06T07:08:44Z","timestamp":1244272124000},"page":"285-303","source":"Crossref","is-referenced-by-count":8,"title":["Meta\u2010learning approach to gene expression data classification"],"prefix":"10.1108","volume":"2","author":[{"given":"Bruno","family":"Feres de Souza","sequence":"first","affiliation":[]},{"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9 C.P.L.F.","family":"de Carvalho","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031720353476600_b1","doi-asserted-by":"crossref","unstructured":"Asyali, M.H., Colak, D., Demirkaya, O. and Inan, M.S. 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