Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/75915
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.creator | Jiang, HM | en_US |
dc.creator | Kwong, CK | en_US |
dc.creator | Park, WY | en_US |
dc.date.accessioned | 2018-05-10T02:54:56Z | - |
dc.date.available | 2018-05-10T02:54:56Z | - |
dc.identifier.issn | 0952-1976 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/75915 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2017 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Jiang, H., Kwong, C. K., & Park, W. Y. (2017). Probabilistic fuzzy regression approach for preference modeling. Engineering Applications of Artificial Intelligence, 64, 286-294 is available at https://doi.org/10.1016/j.engappai.2017.06.019 | en_US |
dc.subject | Probabilistic fuzzy regression | en_US |
dc.subject | Preference modeling | en_US |
dc.subject | Chaos optimization algorithm | en_US |
dc.title | Probabilistic fuzzy regression approach for preference modeling | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 286 | en_US |
dc.identifier.epage | 294 | en_US |
dc.identifier.volume | 64 | en_US |
dc.identifier.doi | 10.1016/j.engappai.2017.06.019 | en_US |
dcterms.abstract | Two types of uncertainty, namely, randomness and fuzziness, exist in preference modeling. Fuzziness is mainly caused by human subjective judgment and incomplete knowledge, and randomness often originates from the variability of influences on the inputs and outputs of a preference model. Various techniques have been utilized to develop preference models. However, only few previous studies have addressed both fuzziness and randomness in preference modeling. Among these limited studies, none have considered the randomness caused by particular independent variables. To fill this research gap, this study proposes probabilistic fuzzy regression (PFR), a new approach for preference modeling. PFR considers both the fuzziness of data sets and the randomness caused by independent variables. In the proposed approach, probability density functions (PDFs) are adopted to model randomness. The parameter settings of the PDFs are determined using a chaos optimization algorithm. The probabilistic terms of the PFR models are generated according to the expected value functions of the random variables. Fuzzy regression analysis is employed to determine the fuzzy coefficients for all the terms of the PFR models. An industrial case study of a tea maker design is used to illustrate the applicability of PFR and evaluate its effectiveness. Modeling results obtained from PFR are compared with those obtained from statistical regression, fuzzy regression, and fuzzy least-squares regression. Results of the training and validation tests show that PFR outperforms the other approaches in terms of training and validation errors. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Engineering applications of artificial intelligence, 2017, v. 64, p. 286-294 | en_US |
dcterms.isPartOf | Engineering applications of artificial intelligence | en_US |
dcterms.issued | 2017 | - |
dc.identifier.isi | WOS:000412378800024 | - |
dc.identifier.eissn | 1873-6769 | en_US |
dc.identifier.rosgroupid | 2017002180 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201805 bcrc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ISE-0780 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6764013 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Kwong_Probabilistic_Fuzzy_Regression.pdf | Pre-Published version | 1.49 MB | Adobe PDF | View/Open |
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