Open AccessMulti‐class classification using kernel density estimation on K ‐nearest neighbours
Author(s) -
Tang Xiaofeng,
Xu Aiqiang
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.4437
Subject(s) - k nearest neighbors algorithm , kernel density estimation , pattern recognition (psychology) , kernel (algebra) , support vector machine , artificial intelligence , parametric statistics , class (philosophy) , computer science , mathematics , density estimation , sample (material) , data mining , statistics , estimator , chemistry , chromatography , combinatorics
A fast and accurate multi‐class classification method based on the conventional kernel density estimation (KDE) and K ‐nearest neighbour (KNN) techniques is proposed. This method estimates the cumulative probabilities of the test sample on its KNNs which may belong to different classes, then selects the maximum weighted class as the classification result. Experiments are carried out to diagnose multiple parametric faults in an analogue circuit, and the classification performances of the proposed method as well as KNN, KDE and support vector machine are compared with each other in detail. The results show that the proposed method is generally better than the other methods not only in classification accuracy but also in test speed, and is promising for practical use.