A multi-average based pseudo nearest neighbor classifier

Article type: Research Article

Authors: Li, Dapeng^{; *} | Guo, Jing

Affiliations: School of Software Engineering, Jinling Institute of Technology, Nanjing, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Conventional k nearest neighbor (KNN) rule is a simple yet effective method for classification, but its classification performance is easily degraded in the case of small size training samples with existing outliers. To address this issue, A multi-average based pseudo nearest neighbor classifier (MAPNN) rule is proposed. In the proposed MAPNN rule, k(k−1)/2 (k>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−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.

Keywords: Multi-average, K-nearest neighbor, small size training samples

DOI: 10.3233/AIC-230312

Journal: AI Communications, vol. 37, no. 4, pp. 677-691, 2024