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A Constrained Optimization based Extreme Learning Machine for noisy data regression

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dc.citedby28
dc.contributor.authorYuong Wong S.en_US
dc.contributor.authorSiah Yap K.en_US
dc.contributor.authorJen Yap H.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.date.accessioned2023-05-29T06:14:06Z
dc.date.available2023-05-29T06:14:06Z
dc.date.issued2016
dc.descriptionArtificial 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 machineen_US
dc.description.abstractMost 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.neucom.2015.07.065
dc.identifier.epage1443
dc.identifier.scopus2-s2.0-84944463466
dc.identifier.spage1431
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84944463466&doi=10.1016%2fj.neucom.2015.07.065&partnerID=40&md5=a0d84d6f5a1df2b69bfc1cc373100ee3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23006
dc.identifier.volume171
dc.publisherElsevieren_US
dc.sourceScopus
dc.sourcetitleNeurocomputing
dc.titleA Constrained Optimization based Extreme Learning Machine for noisy data regressionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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