Back to Search Start Over

Neuro‐weighted multi‐functional nearest‐neighbour classification.

Authors :
Yue, Guanli
Qu, Yanpeng
Deng, Ansheng
Zhang, Qianyi
Source :
Expert Systems; Jun2023, Vol. 40 Issue 5, p1-17, 17p
Publication Year :
2023

Abstract

Background: The performance of nearest‐neighbour classification is highly sensitive to the quality of data. In order to reduce the impact of the inevitable existence of irrelevant features in data, this paper employs the information learned by neural networks to implement a feature weighting technique and associated weighted multi‐functional nearest‐neighbour classification method. Method: The non‐iterative neural networks are easy to implement while enjoying remarkable computational efficiency. In this paper, four non‐iterative neural networks (ELM, E‐ELM, RAWN and RVFL) are employed to evaluate the significance of features to decisions learned and stored in the parameters of neural networks. Moreover, the bias of the significance of features is comforted by using cross‐validation. The resulting feature significance is converted into feature weights to further implement weighted multi‐functional nearest‐neighbour classification. Result: The experimental results demonstrate that the proposed neuro‐weighted feature weighting strategy can effectively reduce the impact of irrelevant features and enhance the performance of multi‐functional nearest‐neighbour classification. Contribution: The proposed algorithm explores an avenue to efficiently utilize the learned knowledge of neural networks to reduce the role of irrelevant feature in nearest‐neighbour classification. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
CLASSIFICATION
DATA quality

Details

Language :
English
ISSN :
02664720
Volume :
40
Issue :
5
Database :
Complementary Index
Journal :
Expert Systems
Publication Type :
Academic Journal
Accession number :
163667995
Full Text :
https://doi.org/10.1111/exsy.13125