{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:36:19Z","timestamp":1777703779745,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2016,3,1]],"date-time":"2016-03-01T00:00:00Z","timestamp":1456790400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2016,3]]},"abstract":"<jats:p>Fuzzy regression models have commonly been used to correlate engineering characteristics with consumer preferences regarding a new product. Based on the models, product developers can determine optimal engineering characteristics of the new product in order to satisfy consumer preferences. However, they have a common limitation in that they cannot guarantee to include significant regressors with significant engineering characteristics or significant nonlinear terms. The generalization capability of the model can be reduced, when too few significant regressors are included and too many insignificant regressors are included. In this paper, a forward selection based fuzzy regression (FS-FR) is proposed based on the statistical forward selection to determine significant regressors. After the significant regressors are determined, the fuzzy regression is used to generate the fuzzy coefficients which address the uncertainties due to fuzziness and randomness caused by consumer preference evaluations. The developed model includes only significant regressors which attempt to improve the generalization capability. A case study of a tea maker design demonstrated that the FS-FR was able to generate consumer preference models with better generalization capabilities than the other tested fuzzy regressions. Also simpler consumer preference models can be provided for the new product development.<\/jats:p>","DOI":"10.3233\/ifs-151898","type":"journal-article","created":{"date-parts":[[2016,3,1]],"date-time":"2016-03-01T10:50:53Z","timestamp":1456829453000},"page":"1869-1880","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences"],"prefix":"10.1177","volume":"30","author":[{"given":"Kit Yan","family":"Chan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Curtin University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sai Ho","family":"Ling","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,3]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"63","article-title":"The house of quality","volume":"66","author":"Hauser J.R.","year":"1988","unstructured":"HauserJ.R. and ClausingD., The house of quality, Harvard Business Review 66 (1988), 63\u201373.","journal-title":"Harvard Business Review"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.12.056"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.12.1.1"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27476-3"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.09.017"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2005.12.007"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-384"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2007.11.017"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2004.01.006"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2012-0507"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(99)00048-X"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2010-0466"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.08.010"},{"key":"e_1_3_1_15_2","first-page":"4747","article-title":"Apply fuzzy regression to model functional relationship in product planning","author":"Chen Y.","year":"2003","unstructured":"ChenY., TangJ., FungR.Y.K. and RenZ., Apply fuzzy regression to model functional relationship in product planning, Proceed the IEEE Conf Decis Cont (2003), 4747\u20134752.","journal-title":"Proceed the IEEE Conf Decis Cont"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207540310001619623"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2005.05.032"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/09544820802563234"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.2298\/SJEE1002253S"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2012.2236841"},{"key":"e_1_3_1_21_2","volume-title":"Applied Regression Analysis: A Research Tool","author":"Rawlings J.O.","year":"1988","unstructured":"RawlingsJ.O., Applied Regression Analysis: A Research Tool, Wadsworth and Brooks, 1988."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2008.926486"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/09544820903550924"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(99)00092-5"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02925308"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2012.05.017"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2012-0523"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/19.836316"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2007.903324"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(88)90054-1"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90144-9"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1021\/ie049626e"}],"container-title":["Journal of Intelligent &amp; 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