Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction

Case adaptation is crucial for case-based reasoning (CBR) because the solutions of old cases are not always the ideal answer for the encountered new problem. It is employed to solve new problems by utilizing the adaptation knowledge extracted from similar ones encountered in the past. The traditiona...

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Autores principales:	Mingchi Lin, Dubo He, Shengxiang Sun
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Case-based reasoning maintenance cost prediction case adaptation multi-output support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c8fa8558a1e64cbcab581ec844d80136
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spelling	oai:doaj.org-article:c8fa8558a1e64cbcab581ec844d801362021-11-18T00:00:52ZMultivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction2169-353610.1109/ACCESS.2021.3117585https://doaj.org/article/c8fa8558a1e64cbcab581ec844d801362021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9558858/https://doaj.org/toc/2169-3536Case adaptation is crucial for case-based reasoning (CBR) because the solutions of old cases are not always the ideal answer for the encountered new problem. It is employed to solve new problems by utilizing the adaptation knowledge extracted from similar ones encountered in the past. The traditional adaptation method solves a new problem in the principle of <italic>k-</italic>nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN), and the adaptation model was built based on <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> similar cases. Yet, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> similar cases retrieved by new case may locate in different case clusters in the case base composed of multiple case clusters. This article presents a new case adaptation method by the combination of multi-adaptation engines from different case clusters to improve the adaptation accuracy. First, the input and output of the cluster-based adaptation engine are established from the old cases to distill the adaptation knowledge in each case cluster. Then, the multivariable CBR adaptation engine based on multiple-output support vector regression (MSVR) is built for case adaptation. Furthermore, inspired by the fact that the training sample which contains two closet cases can provide more useful information than others, and reduce the impact of outliers, this study adds the hybrid weight into MSVR, and allocates high weights to the information provided by such high sample density and similarity samples during multi-dimensional regression estimation. Finally, the solution of the target case is gathered by incorporating the output of different adaptation engines. The proposed method was applied to the equipment maintenance cost prediction and compared with traditional statistical-based and machine learning-based methods. Empirical comparison results indicated that the proposed adaptation method could achieve the best performance by utilizing the adaptation knowledge in different clusters under multi-case clusters environment.Mingchi LinDubo HeShengxiang SunIEEEarticleCase-based reasoningmaintenance cost predictioncase adaptationmulti-output support vector machineElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151960-151971 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Case-based reasoning maintenance cost prediction case adaptation multi-output support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Case-based reasoning maintenance cost prediction case adaptation multi-output support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mingchi Lin Dubo He Shengxiang Sun Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
description	Case adaptation is crucial for case-based reasoning (CBR) because the solutions of old cases are not always the ideal answer for the encountered new problem. It is employed to solve new problems by utilizing the adaptation knowledge extracted from similar ones encountered in the past. The traditional adaptation method solves a new problem in the principle of <italic>k-</italic>nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN), and the adaptation model was built based on <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> similar cases. Yet, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> similar cases retrieved by new case may locate in different case clusters in the case base composed of multiple case clusters. This article presents a new case adaptation method by the combination of multi-adaptation engines from different case clusters to improve the adaptation accuracy. First, the input and output of the cluster-based adaptation engine are established from the old cases to distill the adaptation knowledge in each case cluster. Then, the multivariable CBR adaptation engine based on multiple-output support vector regression (MSVR) is built for case adaptation. Furthermore, inspired by the fact that the training sample which contains two closet cases can provide more useful information than others, and reduce the impact of outliers, this study adds the hybrid weight into MSVR, and allocates high weights to the information provided by such high sample density and similarity samples during multi-dimensional regression estimation. Finally, the solution of the target case is gathered by incorporating the output of different adaptation engines. The proposed method was applied to the equipment maintenance cost prediction and compared with traditional statistical-based and machine learning-based methods. Empirical comparison results indicated that the proposed adaptation method could achieve the best performance by utilizing the adaptation knowledge in different clusters under multi-case clusters environment.
format	article
author	Mingchi Lin Dubo He Shengxiang Sun
author_facet	Mingchi Lin Dubo He Shengxiang Sun
author_sort	Mingchi Lin
title	Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
title_short	Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
title_full	Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
title_fullStr	Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
title_full_unstemmed	Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction
title_sort	multivariable case adaptation method of case-based reasoning based on multi-case clusters and multi-output support vector machine for equipment maintenance cost prediction
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c8fa8558a1e64cbcab581ec844d80136
work_keys_str_mv	AT mingchilin multivariablecaseadaptationmethodofcasebasedreasoningbasedonmulticaseclustersandmultioutputsupportvectormachineforequipmentmaintenancecostprediction AT dubohe multivariablecaseadaptationmethodofcasebasedreasoningbasedonmulticaseclustersandmultioutputsupportvectormachineforequipmentmaintenancecostprediction AT shengxiangsun multivariablecaseadaptationmethodofcasebasedreasoningbasedonmulticaseclustersandmultioutputsupportvectormachineforequipmentmaintenancecostprediction
_version_	1718425251067461632

Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction

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