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In the proposed MAPNN rule, k ( k \u2212 1 ) \/ 2 ( k &gt; 1) local mean vectors of each class are obtained by taking the average of two points randomly from k nearest neighbors in every category, and then k pseudo nearest neighbors are chosen from k ( k \u2212 1 ) \/ 2 local mean neighbors of every class to determine the category of a query point. The selected k pseudo nearest neighbors can reduce the negative impact of outliers in some degree. Extensive experiments are carried out on twenty-one numerical real data sets and four artificial data sets by comparing MAPNN to other five KNN-based methods. The experimental results demonstrate that the proposed MAPNN is effective for classification task and achieves better classification results in the small-size samples cases comparing to five relative KNN-based classifiers.<\/jats:p>","DOI":"10.3233\/aic-230312","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T12:37:55Z","timestamp":1711715875000},"page":"677-691","source":"Crossref","is-referenced-by-count":0,"title":["A multi-average based pseudo nearest neighbor classifier"],"prefix":"10.1177","volume":"37","author":[{"given":"Dapeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Software Engineering, Jinling Institute of Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Jinling Institute of Technology, Nanjing, 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