Open AccessComposite reduced-kernel weighted extreme learning machine for imbalanced data classification
Author(s) -
Dafei Wang,
Wei Xie,
Wenhan Dong
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052108
Subject(s) - extreme learning machine , artificial intelligence , machine learning , kernel (algebra) , radial basis function kernel , computer science , kernel method , polynomial kernel , tree kernel , kernel embedding of distributions , binary classification , kernel principal component analysis , algorithm , pattern recognition (psychology) , mathematics , support vector machine , artificial neural network , combinatorics
In order to solving the problem that the weighted extreme learning machine based on the ensemble learning method enhances the classification performance while increasing the running time of the algorithm, starting from the perspective of multi-core learning, a weighted extreme learning machine based on composite kernel functions and reduced-kernel technique is proposed. The composite kernel function based on Gaussian kernel and Polynomial kernel weighted combination is designed, which effectively improves the classification performance of weighted extreme learning machine. Meanwhile, based on the sample distribution characteristics of the imbalanced dataset, the balanced input sub-matrix is designed to reduce the computational cost of the composite kernel method. The eight binary classification imbalanced datasets of KEEL dataset repository were used for testing. The experimental results show that compared with the original weighted extreme learning machine algorithm, the G- mean and AUC classification performance indicators of the composite reduced-kernel weighted extreme learning machine algorithm are improved in each dataset, and the computation cost is effectively reduced.