Multi-class classification using kernel density estimation on K-nearest

作者：Tang Xiaofeng; Xu Aiqiang^*

来源：Electronics Letters, 2016, 52(8): 600-601.

DOI：10.1049/el.2015.4437

• analogue circuits
• pattern classification
• fault diagnosis
• higher order statistics
• support vector machines
• circuit analysis computing
• kernel density estimation
• K-nearest neighbours
• multiclass classification method
• KDE
• KNN technique
• cumulative probabilities
• multiple parametric fault diagnosis
• analogue circuit
• support vector machine

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.

• 出版日期2016-4-14
• 单位中国人民解放军海军航空工程学院