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As is well known, k-nearest neighbor classifiers are simple to implement and conceptually simple to implement. It is not without its shortcomings, however, as follows: (1) there is still a sensitivity to the choice of <jats:italic>k<\/jats:italic>-values even when representative attributes are not considered in each class; (2) in some cases, the proximity between test samples and nearest neighbor samples cannot be reflected accurately due to proximity measurements, etc. Here, we propose a non-parametric nearest neighbor classification method based on global variance differences. First, the difference in variance is calculated before and after adding the sample to be the subject, then the difference is divided by the variance before adding the sample to be tested, and the resulting quotient serves as the objective function. In the final step, the samples to be tested are classified into the class with the smallest objective function. Here, we discuss the theoretical aspects of this function. Using the Lagrange method, it can be shown that the objective function can be optimal when the sample centers of each class are averaged. Twelve real datasets from the University of California, Irvine are used to compare the proposed algorithm with competitors such as the Local mean k-nearest neighbor algorithm and the pseudo-nearest neighbor algorithm. According to a comprehensive experimental study, the average accuracy on 12 datasets is as high as 86.27<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, which is far higher than other algorithms. The experimental findings verify that the proposed algorithm produces results that are more dependable than other existing algorithms.<\/jats:p>","DOI":"10.1007\/s44196-023-00200-1","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T13:02:56Z","timestamp":1677762176000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Non-parametric Nearest Neighbor Classification Based on Global Variance Difference"],"prefix":"10.1007","volume":"16","author":[{"given":"Shaobo","family":"Deng","sequence":"first","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Sujie","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"200_CR1","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1007\/s12652-022-03774-4","volume":"13","author":"S Dong","year":"2022","unstructured":"Dong, S., Sarem, M.: Nocd: a new overlapping community detection algorithm based on improved knn. 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To my\/our\u2014and currently accepted scientific\u2014knowledge, all statements contained in it purporting to be facts are true and any formula or instruction contained in the article will not, if followed accurately, cause any injury, illness or damage to the user. Signature: WangLei Date:2022.3.8.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"I, and all the co-authors, agree that the article, if editorially accepted for publication, shall be licensed under the Creative Commons Attribution License 4.0. If the law requires that the article be published in the public domain, I\/we will notify Springer at the time of submission, and in such cases the article shall be released under the Creative Commons 1.0 Public Domain Dedication waiver. For the avoidance of doubt, it is stated that sections 1 and 2 of this license agreement shall apply and prevail regardless of whether the article is published under Creative Commons Attribution License 4.0 or the Creative Commons 1.0 Public Domain Dedication waiver. I, and all co-authors, agree that, if the article is editorially accepted for publication in Chemistry Central Journal, Chemical and Biological Technologies in Agriculture, Geochemical Transactions, Heritage Science, Journal of Cheminformatics, or Sustainable Chemical Processes, data included in the article shall be made available under the Creative Commons 1.0 Public Domain Dedication waiver, unless otherwise stated. Signature: WangLei Date: 2020.3.8.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Due to the large number of algorithm codes, the codes generated and analyzed during this study are not public, but can be obtained from the corresponding authors according to reasonable requirements.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}],"article-number":"26"}}