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In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method\u2019s performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods.<\/jats:p>","DOI":"10.1093\/jigpal\/jzac018","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T13:38:13Z","timestamp":1644932293000},"page":"271-286","source":"Crossref","is-referenced-by-count":0,"title":["Filtering non-balanced data using an evolutionary approach"],"prefix":"10.1093","volume":"31","author":[{"given":"Jessica A","family":"Carballido","sequence":"first","affiliation":[{"name":"Institute for Computer Science and Engineering (UNS\u2013CONICET) , Department of Computer Science and Engineering, Universidad Nacional del Sur, San Andr\u00e9s 800, 8000, Bah\u00eda Blanca, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignacio","family":"Ponzoni","sequence":"additional","affiliation":[{"name":"Institute for Computer Science and Engineering (UNS\u2013CONICET) , Department of Computer Science and Engineering, Universidad Nacional del Sur, San Andr\u00e9s 800, 8000, Bah\u00eda Blanca, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roc\u00edo L","family":"Cecchini","sequence":"additional","affiliation":[{"name":"Institute for Computer Science and Engineering (UNS\u2013CONICET) , Department of Computer Science and Engineering, Universidad Nacional del Sur, San Andr\u00e9s 800, 8000, Bah\u00eda Blanca, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"2023033115514457100_","doi-asserted-by":"crossref","first-page":"000319","DOI":"10.1109\/CINTI.2016.7846426","article-title":"Efficient instance selection algorithm for classification based on fuzzy frequent patterns","volume-title":"2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI)","author":"Alvar","year":"2016"},{"key":"2023033115514457100_","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TFUZZ.2011.2173582","article-title":"Genetic training instance selection in multiobjective evolutionary fuzzy systems: a coevolutionary approach","volume":"20","author":"Antonelli","year":"2012","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"2023033115514457100_","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ins.2013.12.029","article-title":"Feature selection with SVD entropy: some modification and extension","volume":"264","author":"Banerjee","year":"2014","journal-title":"Information Sciences"},{"key":"2023033115514457100_","first-page":"2225","article-title":"VAT: a tool for visual assessment of (cluster) tendency","volume-title":"Proceedings of the 2002 International Joint Conference on Neural Networks. 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