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When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of \u201cincremental learning\u201d addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model\u2019s knowledge of genomes grows, classification accuracy increases. The proof-of-concept na\u00efve Bayes implementation, when updated yearly, now runs in 1\/4\n                      <jats:sup>\n                        <jats:italic>t<\/jats:italic>\n                        <jats:italic>h<\/jats:italic>\n                      <\/jats:sup>\n                      of the non-incremental time with no accuracy loss.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-03744-7","type":"journal-article","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T08:03:26Z","timestamp":1600675406000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life"],"prefix":"10.1186","volume":"21","author":[{"given":"Zhengqiao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru","family":"Cristian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1763-5750","authenticated-orcid":false,"given":"Gail","family":"Rosen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,21]]},"reference":[{"key":"3744_CR1","unstructured":"Zynda GJ. 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