The Distance-Weighted k-Nearest-Neighbor Rule

作者：Sahibsingh A. Dudani · 发表于：IEEE Transactions on Systems Man and Cybernetics · 年份：1976 · DOI：10.1109/tsmc.1976.5408784 · 被引用次数：1471 · 研究领域：Advanced Statistical Methods and Models、Anomaly Detection Techniques and Applications、Imbalanced Data Classification Techniques

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.