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

[21] Outsourcing of Secure k-Nearest Neighbours Interpolation Method
Kalideen, Muhammad Rifthy
Tugrul, Bulent
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2018, 9 (04) : 319 - 323
[22] Computing Expectiles Using k-Nearest Neighbours Approach
Farooq, Muhammad
Sarfraz, Sehrish
Chesneau, Christophe
Ul Hassan, Mahmood
Raza, Muhammad Ali
Sherwani, Rehan Ahmad Khan
Jamal, Farrukh
SYMMETRY-BASEL, 2021, 13 (04):
[23] Bagged k-nearest neighbours classification with uncertainty in the variables
Villa Medina, Joe L.
Boque, Ricard
Ferre, Joan
ANALYTICA CHIMICA ACTA, 2009, 646 (1-2) : 62 - 68
[24] Continuous k-Nearest Neighbour Strategies Using the mqrtree
Osborn, Wendy
ADVANCES IN NETWORK-BASED INFORMATION SYSTEMS, NBIS-2018, 2019, 22 : 168 - 181
[25] k-Nearest Neighbour method in functional nonparametric regression
Burba, Florent
Ferraty, Frederic
Vieu, Philippe
JOURNAL OF NONPARAMETRIC STATISTICS, 2009, 21 (04) : 453 - 469
[26] An empirical analysis of the probabilistic K-nearest neighbour classifier
Manocha, S.
Girolami, M. A.
PATTERN RECOGNITION LETTERS, 2007, 28 (13) : 1818 - 1824
[27] Outlier detection using k-nearest neighbour graph
Hautamäki, V
Kärkkäinen, I
Fränti, P
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, 2004, : 430 - 433
[28] Improving the k-nearest neighbour rule: using geometrical neighbourhoods and manifold-based metrics
Altincay, Hakan
EXPERT SYSTEMS, 2011, 28 (04) : 391 - 406
[29] A GENERALIZED K-NEAREST NEIGHBOR RULE
PATRICK, EA
FISCHER, FP
INFORMATION AND CONTROL, 1970, 16 (02): : 128 - &
[30] K-nearest neighbors rule combining prototype selection and local feature weighting for classification
Zhang, Xin
Xiao, Hongshan
Gao, Ruize
Zhang, Hongwu
Wang, Yu
KNOWLEDGE-BASED SYSTEMS, 2022, 243

← 1 2 3 4 5 →