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A New Probabilistic Output Constrained Optimization Extreme Learning Machine

Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning...

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Main Authors: Wong S.Y., Yap K.S., Li X.C.
Other Authors: 55812054100
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-257672023-05-29T16:14:02Z A New Probabilistic Output Constrained Optimization Extreme Learning Machine Wong S.Y. Yap K.S. Li X.C. 55812054100 24448864400 23100514300 Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning approaches; Pattern classification problems; Post-processing procedure; Power system applications; Probabilistic output; Machine learning In near decades machine learning approaches have received overwhelming attention from many researchers for solving problems that cannot be ironed out by traditional approaches. However, most of these approaches produces output that is not equivalent to the probability estimates of how credible and reliable the output can be for each prediction. One widely utilized, highly accorded for generalized performance but non-probabilistic machine learning algorithm is the Extreme Learning Machine (ELM). As with other classification systems, ELM generates outputs that cannot be treated as probabilities. Current literature shows approaches attempt to assimilate probabilistic concept in ELM however their outputs are not equivalent to probabilities. Furthermore, these methods invoke two-stage post processing procedures with iterative learning procedures which are against the salient features of ELM that highlight no iterative operations involved in learning. Hence, we want to probe in this paper the ability of ELM to produce probabilistic output from the original architecture of ELM itself while preserving the merits of ELM without the need for a post processing two-stage procedures to convert the output to probability and eliminates iterative learning to compute output weights. Two methodologies of unified probabilistic ELM framework are presented, i.e., Probabilistic Output Extreme Learning Machine (PO-ELM) and Constrained Optimization Posterior Probabilistic Outputs based Extreme Learning Machine (CPP-POELM). The proposed models are evaluated empirically on several benchmark datasets as well as real world power system applications to demonstrate its validity and efficacy in handling pattern classification problems as well as decision making process. � 2013 IEEE. Final 2023-05-29T08:14:02Z 2023-05-29T08:14:02Z 2020 Article 10.1109/ACCESS.2020.2971012 2-s2.0-85079821759 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079821759&doi=10.1109%2fACCESS.2020.2971012&partnerID=40&md5=ad5a9ad1a3dd0423e310bf8c077de6bd https://irepository.uniten.edu.my/handle/123456789/25767 8 8978896 28934 28946 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning approaches; Pattern classification problems; Post-processing procedure; Power system applications; Probabilistic output; Machine learning
author2 55812054100
author_facet 55812054100
Wong S.Y.
Yap K.S.
Li X.C.
format Article
author Wong S.Y.
Yap K.S.
Li X.C.
spellingShingle Wong S.Y.
Yap K.S.
Li X.C.
A New Probabilistic Output Constrained Optimization Extreme Learning Machine
author_sort Wong S.Y.
title A New Probabilistic Output Constrained Optimization Extreme Learning Machine
title_short A New Probabilistic Output Constrained Optimization Extreme Learning Machine
title_full A New Probabilistic Output Constrained Optimization Extreme Learning Machine
title_fullStr A New Probabilistic Output Constrained Optimization Extreme Learning Machine
title_full_unstemmed A New Probabilistic Output Constrained Optimization Extreme Learning Machine
title_sort new probabilistic output constrained optimization extreme learning machine
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424148385202176
score 13.211869

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