A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)

© 2015 ICIC International. This paper attempts to answer the question “How to construct and apply the novel algorithm based on Ensemble Learning approach called Bayesian Mixed Probability Distributions-CBR-C5.0-CART for Medical Knowledge-Based Systems and Knowledge-Based Systems (KBSs)?” The finding...

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Main Authors:	Patcharaporn Paokanta, Somdet Srichairatanakool
Format:	Journal
Published:	2018
Subjects:	Agricultural and Biological Sciences
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84930248097&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44449
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-44449
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spelling	th-cmuir.6653943832-444492018-04-25T07:51:06Z A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree) Patcharaporn Paokanta Somdet Srichairatanakool Agricultural and Biological Sciences © 2015 ICIC International. This paper attempts to answer the question “How to construct and apply the novel algorithm based on Ensemble Learning approach called Bayesian Mixed Probability Distributions-CBR-C5.0-CART for Medical Knowledge-Based Systems and Knowledge-Based Systems (KBSs)?” The finding of this study is the new algorithm of Bayesian-Mixed Probability Distributions-C5.0-CART which is developed for the inference engines of KBSs. The proposed algorithm is applied to Thalassemia data set including F-cell, HbA < inf > 2 < /inf > , and Inclusion Body of Thalassemia patients. These are collected from medical practitioner and scientist who are the experts in Thalassemia diagnosis. In the future, this algorithm and a new collected data set will be combined with graph theory to generate the new theory called Ramsey Graph Bayesian-Mixed Probability Distributions for Digital Images Processing and Images Processing. 2018-01-24T04:43:04Z 2018-01-24T04:43:04Z 2015-01-01 Journal 13494198 2-s2.0-84930248097 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84930248097&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44449
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Agricultural and Biological Sciences
spellingShingle	Agricultural and Biological Sciences Patcharaporn Paokanta Somdet Srichairatanakool A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
description	© 2015 ICIC International. This paper attempts to answer the question “How to construct and apply the novel algorithm based on Ensemble Learning approach called Bayesian Mixed Probability Distributions-CBR-C5.0-CART for Medical Knowledge-Based Systems and Knowledge-Based Systems (KBSs)?” The finding of this study is the new algorithm of Bayesian-Mixed Probability Distributions-C5.0-CART which is developed for the inference engines of KBSs. The proposed algorithm is applied to Thalassemia data set including F-cell, HbA < inf > 2 < /inf > , and Inclusion Body of Thalassemia patients. These are collected from medical practitioner and scientist who are the experts in Thalassemia diagnosis. In the future, this algorithm and a new collected data set will be combined with graph theory to generate the new theory called Ramsey Graph Bayesian-Mixed Probability Distributions for Digital Images Processing and Images Processing.
format	Journal
author	Patcharaporn Paokanta Somdet Srichairatanakool
author_facet	Patcharaporn Paokanta Somdet Srichairatanakool
author_sort	Patcharaporn Paokanta
title	A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
title_short	A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
title_full	A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
title_fullStr	A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
title_full_unstemmed	A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)
title_sort	new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: hybrid bayesian computing (multinomial logistic regression case-based c5.0-mixed classification and regression tree)
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84930248097&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44449
_version_	1681422561546600448

A new algorithm of ensemble learning for medical knowledge-based systems and knowledge-based systems: Hybrid Bayesian computing (multinomial logistic regression case-based C5.0-mixed classification and regression tree)

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