The Distance-Weighted k-Nearest-Neighbor Rule

1976/04/01 by Sahibsingh A. Dudani · 1,465 citations
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Anomaly Detection Techniques and Applications #Artificial intelligence #Biology #Class (philosophy) #Computer science #Data mining #Function (biology) #Imbalanced Data Classification Techniques #Mathematics #Pattern recognition (psychology) #Physics #Sample (material) #Weighting #k-nearest neighbors algorithm

paper · doi:10.1109/tsmc.1976.5408784

published in IEEE Transactions on Systems Man and Cybernetics SMC-6(4), 325-327 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1976/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Among the simplest and most intuitively appealing classes of nonprobabilistic classification procedures are those that weight the evidence of nearby sample observations most heavily. More specifically, one might wish to weight the evidence of a neighbor close to an unclassified observation more heavily than the evidence of another neighbor which is at a greater distance from the unclassified observation. One such classification rule is described which makes use of a neighbor weighting function for the purpose of assigning a class to an unclassified sample. The admissibility of such a rule is also considered.

Citations

• Nearest neighbor pattern classification

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