On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule

被引：11

作者：

Mateos-Garcia, Daniel ^{[1
]}

Garcia-Gutierrez, Jorge ^{[1
]}

Riquelme-Santos, Jose C. ^{[1
]}

机构：

[1] Univ Seville, Dept Comp Sci, Avda Reina Mercedes S-N, E-41012 Seville, Spain

来源：

NEUROCOMPUTING | 2019年 / 326卷

关键词：

Evolutionary computation; Neighbours weighting; Feature weighting; STATISTICAL COMPARISONS; FEATURE-SELECTION; CLASSIFIERS; NN;

D O I：

10.1016/j.neucom.2016.08.159

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour classifier (kNN) called Simultaneous Weighting of Attributes and Neighbours (SWAN). Unlike other weighting methods, SWAN presents the ability of adjusting the contribution of the neighbours and the significance of the features of the data. The optimization process focuses on the search of two real-valued vectors. One of them represents the votes of neighbours, and the other one represents the weight of each feature. The synergy between the two sets of weights found in the optimization process helps to improve significantly, the classification accuracy. The results on 35 datasets from the UCI repository suggest that SWAN statistically outperforms the other weighted kNN methods. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：54 / 60

页数：7

共 50 条

[41] K-Nearest Neighbours Method as a Tool for Failure Rate Prediction
Kutylowska, Malgorzata
PERIODICA POLYTECHNICA-CIVIL ENGINEERING, 2018, 62 (02): : 318 - 322
[42] A fast fuzzy K-nearest neighbour algorithm for pattern classification
Boutalis, Yiannis S.
Andreadis, Ioannis T.
Tambakis, George D.
Intelligent Data Analysis, 2000, 4 (3-4) : 275 - 288
[43] Prediction of MUET Results Based on K-Nearest Neighbour Algorithm
Sabri N.M.
Hamrizan S.F.A.
Annals of Emerging Technologies in Computing, 2023, 7 (05) : 50 - 59
[44] A novel algorithm for scalable k-nearest neighbour graph construction
Park, Youngki
Hwang, Heasoo
Lee, Sang-goo
JOURNAL OF INFORMATION SCIENCE, 2016, 42 (02) : 274 - 288
[45] EMPIRICAL LIKELIHOOD RATIO BASED K-NEAREST NEIGHBOUR SREGRESSION
Sukshitha, R.
Satyanarayana
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2024, 20 (02): : 421 - 428
[46] A K-NEAREST NEIGHBOURS APPROACH TO UNSUPERVISED SPOKEN TERM DISCOVERY
Thual, Alexis
Dancette, Corentin
Karadayi, Julien
Benjumea, Juan
Dupoux, Emmanuel
2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018), 2018, : 491 - 497
[47] ECG Beat Classification using Evidential K-Nearest Neighbours
Faziludeen, Shameer
Sankaran, Praveen
TWELFTH INTERNATIONAL CONFERENCE ON COMMUNICATION NETWORKS, ICCN 2016 / TWELFTH INTERNATIONAL CONFERENCE ON DATA MINING AND WAREHOUSING, ICDMW 2016 / TWELFTH INTERNATIONAL CONFERENCE ON IMAGE AND SIGNAL PROCESSING, ICISP 2016, 2016, 89 : 499 - 505
[48] Using K-Nearest Neighbours Feature Selection for Activity Recognition
Friedrich, Bjoern
Orsot, Tetchi Ange-Michel
Hein, Andreas
SENSOR- AND VIDEO-BASED ACTIVITY AND BEHAVIOR COMPUTING, ABC 2021, 2022, 291 : 217 - 225
[49] Examining k-nearest neighbour networks: Superfamily phenomena and inversion
Khor, Alexander
Small, Michael
CHAOS, 2016, 26 (04)
[50] A comparative study of k-nearest neighbour techniques in crowd simulation
Vermeulen, Jordi L.
Hillebrand, Arne
Geraerts, Roland
COMPUTER ANIMATION AND VIRTUAL WORLDS, 2017, 28 (3-4)

← 1 2 3 4 5 →