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The Distance-Weighted k-Nearest-Neighbor Rule
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.
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Cited by
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Sun, W., Nasraoui, O., & Shafto, P. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235502 · Cited by 116
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Metadata
- Authors
- Dudani, S. A.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1976-04
- Volume
- SMC-6
- Issue
- 4
- Pages
- 325-327
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2130695501
- Cited by
- 1,471
- References
- 0
- FWCI
- 31.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dudani, S. A. (1976). The Distance-Weighted k-Nearest-Neighbor Rule. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/tsmc.1976.5408784
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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