Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104204
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorJiang, Hen_US
dc.creatorKwong, CKen_US
dc.creatorChan, CYen_US
dc.creatorYung, KLen_US
dc.date.accessioned2024-02-05T08:47:07Z-
dc.date.available2024-02-05T08:47:07Z-
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://hdl.handle.net/10397/104204-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2019. 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.rightsThe following publication Jiang, H., Kwong, C. K., Chan, C. Y., & Yung, K. L. (2019). A multi-objective evolutionary approach for fuzzy regression analysis. Expert Systems with Applications, 130, 225–235 is available at https://doi.org/10.1016/j.eswa.2019.04.033.en_US
dc.subjectFuzzy regressionen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectNSGA-IIen_US
dc.titleA multi-objective evolutionary approach for fuzzy regression analysisen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage225en_US
dc.identifier.epage235en_US
dc.identifier.volume130en_US
dc.identifier.doi10.1016/j.eswa.2019.04.033en_US
dcterms.abstractFuzzy regression analysis was extensively used in previous studies to model the relationships between dependent and independent variables in a fuzzy environment. Various approaches have been proposed to perform fuzzy regression analysis with most of the approaches adopting a single objective function in the generation of fuzzy regression models. Some previous studies attempted to generate fuzzy regression models using a multi-objective optimization approach in order to improve the prediction accuracy of the generated fuzzy regression models. However, in the studies, the subjective judgments of parameter settings are required for solving multi-objective optimization problems and a complete representation of Parato optimal solutions cannot be generated in a single run. To address the limitations, a multi-objective evolutionary approach to fuzzy regression analysis is proposed in this paper. In the proposed approach, a multi-objective optimization problem is formulated which involves three objectives; minimizing the fuzziness of fuzzy outputs, minimizing the effect of outliers and minimizing the mean absolute percentage error of modeling. A non-dominated sorting genetic algorithm-α is introduced to solve the problem and generate a set of Pareto optimal solutions. Finally, a technique for order of preference by similarity to ideal solution is applied to determine a final optimal solution by which a fuzzy regression model can be generated. A case study is conducted to illustrate the proposed approach. Sixteen validation tests are conducted to evaluate the effectiveness of the proposed approach. The results of the validation tests show that the proposed approach outperforms Tanaka's fuzzy regression, Peters’ fuzzy regression, compromise programming based multi-objective fuzzy regression, fuzzy least-squares regression and probabilistic fuzzy regression approaches in terms of training errors and prediction accuracy.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationExpert systems with applications, 15 Sept 2019, v. 130, p. 225-235en_US
dcterms.isPartOfExpert systems with applicationsen_US
dcterms.issued2019-09-15-
dc.identifier.scopus2-s2.0-85064559774-
dc.identifier.eissn1873-6793en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0424-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS24080412-
dc.description.oaCategoryGreen (AAM)en_US
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