Weighted K-Nearest Neighbor Revisited

M. Bicego; Marco Loog

doi:10.1109/ICPR.2016.7899872

Weighted K-Nearest Neighbor Revisited

Pattern Recognition and Bioinformatics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

21 Citations (Scopus)

Abstract

In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition (ICPR)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1642-1647
Number of pages	6
ISBN (Electronic)	978-1-5090-4847-2
ISBN (Print)	978-1-5090-4848-9
DOIs	https://doi.org/10.1109/ICPR.2016.7899872
Publication status	Published - 2016
Event	ICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 Conference number: 23

Conference

Conference	ICPR 2016
Country/Territory	Mexico
City	Cancún
Period	4/12/16 → 8/12/16

Keywords

Training
Diversity reception
Pattern recognition
Electronic mail
Testing
Degradation
Terminology

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10.1109/ICPR.2016.7899872

Cite this

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title = "Weighted K-Nearest Neighbor Revisited",

abstract = "In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.",

keywords = "Training, Diversity reception , Pattern recognition, Electronic mail , Testing, Degradation , Terminology ",

author = "M. Bicego and Marco Loog",

year = "2016",

doi = "10.1109/ICPR.2016.7899872",

language = "English",

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pages = "1642--1647",

booktitle = "2016 23rd International Conference on Pattern Recognition (ICPR)",

publisher = "IEEE",

address = "United States",

note = "ICPR 2016 : 23rd International Conference on Pattern Recognition ; Conference date: 04-12-2016 Through 08-12-2016",

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T1 - Weighted K-Nearest Neighbor Revisited

AU - Bicego, M.

AU - Loog, Marco

N1 - Conference code: 23

PY - 2016

Y1 - 2016

N2 - In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.

AB - In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.

KW - Training

KW - Diversity reception

KW - Pattern recognition

KW - Electronic mail

KW - Testing

KW - Degradation

KW - Terminology

U2 - 10.1109/ICPR.2016.7899872

DO - 10.1109/ICPR.2016.7899872

M3 - Conference contribution

SN - 978-1-5090-4848-9

SP - 1642

EP - 1647

BT - 2016 23rd International Conference on Pattern Recognition (ICPR)

PB - IEEE

CY - Piscataway, NJ

T2 - ICPR 2016

Y2 - 4 December 2016 through 8 December 2016

ER -

Weighted K-Nearest Neighbor Revisited

Abstract

Conference

Keywords

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Cite this