Open AccessPinball loss based extreme learning machines
Author(s) -
Kuaini Wang,
Xiaoshuai Ding
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/569/5/052061
Subject(s) - extreme learning machine , outlier , robustness (evolution) , computer science , mean squared error , benchmark (surveying) , quantile , artificial neural network , machine learning , artificial intelligence , feedforward neural network , function (biology) , pattern recognition (psychology) , algorithm , mathematics , statistics , chemistry , biochemistry , geodesy , evolutionary biology , biology , gene , geography
Extreme Learning Machine (ELM) is a novel machine learning method by training single hidden layer feedforward neural network. It employs squared loss function to minimize the mean squares error, which is sensitive to noises and outliers. In this paper, pinball loss function with quantile error is introduced into ELM in order to improve the robustness of ELM. An ELM model based on squared pinball loss function (SPELM) and an ELM model based on pinball loss function (PELM) are proposed. The corresponding optimization problem are solved by iterative reweighted algorithm. Three simulated datasets and nine Benchmark datasets are used to verify the validity of the proposed models. It is concluded that the proposed SPELM and PELM are superior to other comparisons, especially for datasets containing larger proportion of outliers.