The Distance-Weighted k-Nearest-Neighbor Rule

The Distance-Weighted k-Nearest-Neighbor Rule

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 heav...

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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1976-04, Vol.SMC-6 (4), p.325-327
1. Verfasser: Dudani, Sahibsingh A.
Format: Artikel
Sprache:eng
Schlagworte:
Error correction
H infinity control
Nearest neighbor searches
Upper bound
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container_title IEEE transactions on systems, man, and cybernetics
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creator Dudani, Sahibsingh A.
description 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.
doi_str_mv 10.1109/TSMC.1976.5408784
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identifier ISSN: 0018-9472
ispartof IEEE transactions on systems, man, and cybernetics, 1976-04, Vol.SMC-6 (4), p.325-327
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source IEEE Electronic Library (IEL)
subjects Error correction
H infinity control
Nearest neighbor searches
Upper bound
title The Distance-Weighted k-Nearest-Neighbor Rule
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