Publication: A Constrained Optimization based Extreme Learning Machine for noisy data regression
Date
2016
Authors
Yuong Wong S.
Siah Yap K.
Jen Yap H.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Most of the existing Artificial Intelligence (AI) models for data regression commonly assume that the data samples are completely clean without noise or worst yet, only the symmetrical noise is in considerations. However in the real world applications, this is often not the case. This paper addresses a significant note of inefficiency in methods for regression when dealing with outliers, especially for cases with polarity of noise involved (i.e., one sided noise with either only positive noise or negative noise). Using soft margin loss function concept, we propose Constrained Optimization method based Extreme Learning Machine for Regression, hereafter denoted as CO-ELM-R. The proposed method incorporates the two Lagrange multipliers that mimic Support Vector Regression (SVR) into the basis of ELM to cope with infeasible constraints of the regression optimization problem. Thus, CO-ELM-R will complement the recursive iterations of SVR in the training phase due to the fact that ELM is much simpler in structure and faster in implementation. The proposed CO-ELM-R is evaluated empirically on a few benchmark data sets and a real world application of NO. x gas emission data set collected from one of the power plant in Malaysia. The obtained results have demonstrated its validity and efficacy in handling noisy data regression problems. � 2015 Elsevier B.V.
Description
Artificial intelligence; Benchmarking; Data handling; Knowledge acquisition; Lagrange multipliers; Learning systems; Optimization; Regression analysis; Benchmark data; Constrained optimization methods; Data regression; Extreme learning machine; Kernel function; Noisy data; Optimization problems; Support vector regression (SVR); Constrained optimization; nitric oxide; algorithm; Article; artificial intelligence; artificial neural network; classifier; combustion; entropy; exhaust gas; extreme learning machine; fuzzy system; generalized regression neural network; generalized regression neural network and fuzzy art; housing; kernel method; logistic regression analysis; machine learning; Malaysia; priority journal; probabilitistic entropy based neural network; process optimization; radial based function; regression analysis; support vector machine