A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences. (English) Zbl 1361.62059
Summary: 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 thestatistical 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.
MSC:
| 62P20 | Applications of statistics to economics |
| 62J86 | Fuzziness, and linear inference and regression |
| 91B08 | Individual preferences |
Keywords:
fuzzy regression; statistical forward selection; new product development; consumer preferences; engineering characteristics; tea maker design; overfitting; generalization capabilityReferences:
| [1] | Hauser, The house of quality, Harvard Business Review 66 pp 63– (1988) |
| [2] | Chan, A fuzzy ordinary regression method for modeling customer preference in tea maker design, Neurocomputing 142 (22) pp 147– (2014) · doi:10.1016/j.neucom.2013.12.056 |
| [3] | Griffin, The voice of the customer, Marketing Sciences 12 (1) pp 1– (1993) · doi:10.1287/mksc.12.1.1 |
| [4] | Chan, Computational Intelligence Techniques for New Product Design (2012) · doi:10.1007/978-3-642-27476-3 |
| [5] | Grigoroudis, The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach, European Journal of Operational Research 187 (3) pp 1346– (2008) · Zbl 1137.90545 · doi:10.1016/j.ejor.2006.09.017 |
| [6] | You, Development of customer satisfaction models for automotive interior materials, International Journal of Industrial Ergonomics 36 (4) pp 323– (2006) · doi:10.1016/j.ergon.2005.12.007 |
| [7] | Liu, Multilocus association mapping using generalized ridge logistic regression, BMC Bioinformatics 12 pp 384– (2011) · doi:10.1186/1471-2105-12-384 |
| [8] | Liu, A fuzzy model for customer satisfaction index in e-commerce, Mathematics and Computers in Simulation 77 (5-6) pp 512– (2008) · Zbl 1133.91464 · doi:10.1016/j.matcom.2007.11.017 |
| [9] | Park, A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design, International Journal of Industrial Ergonomics 34 (1) pp 31– (2004) · doi:10.1016/j.ergon.2004.01.006 |
| [10] | Karakasidis, Fuzzy regression analysis: An application on tensile strength of materials and hardness scales, Journal of Intelligent & Fuzzy Systems 23 (5) pp 177– (2012) |
| [11] | Kim, Fuzzy multi-criteria models for quality function deployment, European Journal of Operational Research 121 (3) pp 504– (2000) · Zbl 0968.90045 · doi:10.1016/S0377-2217(99)00048-X |
| [12] | Sekkeli, Classification models based on Tanaka’s fuzzy linear regression approach: The case of customer satisfaction modeling, Journal of Intelligent & Fuzzy Systems 21 (5) pp 341– (2010) |
| [13] | Chen, A two-stage approach for formulating fuzzy regression models, Knowledge-Based Systems 52 pp 302– (2013) · doi:10.1016/j.knosys.2013.08.010 |
| [14] | Chen, Apply fuzzy regression to model functional relationship in product planning, Proceed the IEEE Conf Decis Cont pp 4747– (2003) |
| [15] | Chen, Fuzzy regression-based mathematical programming model for quality function deployment, International Journal of Production Research 42 (5) pp 1009– (2004) · Zbl 1069.90118 · doi:10.1080/00207540310001619623 |
| [16] | Fung, Estimating functional relationships for product planning under uncertainties, Fuzzy Sets and Systems 157 (1) pp 98– (2006) · Zbl 1085.90017 · doi:10.1016/j.fss.2005.05.032 |
| [17] | Kwong, A generalised fuzzy least-squares regression approach to modelling relationships in QFD, Journal of Engineering Design 21 (5) pp 601– (2010) · doi:10.1080/09544820802563234 |
| [18] | Safari, Robust state feedback controller design of STATCOM using chaotic optimization algorithm, Serbian Journal of Electrical Engineering 7 (2) pp 253– (2010) · doi:10.2298/SJEE1002253S |
| [19] | Jiang, Chaos-based fuzzy regression approach to modelling customer satisfaction for product design, IEEE Transactions on Fuzzy Systems 21 (5) pp 926– (2013) · doi:10.1109/TFUZZ.2012.2236841 |
| [20] | Rawlings, Applied Regression Analysis: A Research Tool (1988) · Zbl 0672.62076 |
| [21] | Vladislavleva, Order of nonlinearity as complexity measure for models generated by symbolic regression via pareto genetic programming, IEEE Transactions on Evolutionary Computation 13 (2) pp 333– (2009) · doi:10.1109/TEVC.2008.926486 |
| [22] | Chan, An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness, Journal of Engineering Design 22 (8) pp 523– (2011) · doi:10.1080/09544820903550924 |
| [23] | Chang, Hybrid fuzzy least-squares regression analysis and its reliability measures, Fuzzy Sets and Systems 119 (2) pp 225– (2001) · Zbl 0997.62051 · doi:10.1016/S0165-0114(99)00092-5 |
| [24] | Johnsson, A procedure for stepwise regression analysis, Statistical Papers 33 pp 21– (1992) · doi:10.1007/BF02925308 |
| [25] | Hong, Piecewise regression model construction with sample efficient regression tree (SERT) and applications to semiconductor yield analysis, Journal of Process Control 22 (7) pp 1307– (2006) · doi:10.1016/j.jprocont.2012.05.017 |
| [26] | Soroush, A hybrid customer prediction system based on multiple forward stepwise logistic regression model, Intelligent Data Analysis 16 (2) pp 265– (2012) |
| [27] | Mauris, Fuzzy handling of measurement errors in instrumentation, IEEE Transactions on Instrumentation and Measurement 49 (1) pp 89– (2000) · doi:10.1109/19.836316 |
| [28] | Kwong, The hybrid fizzy least-squares regression approach to modeling manufacturing processes, IEEE Transactions on Fuzzy Systems 16 (3) pp 644– (2008) · doi:10.1109/TFUZZ.2007.903324 |
| [29] | Tanaka, Possibilistic linear systems and their application to the linear regression model, Fuzzy Sets and Systems 27 (3) pp 275– (1988) · Zbl 0662.93066 · doi:10.1016/0165-0114(88)90054-1 |
| [30] | Peters, Fuzzy linear regression with fuzzy intervals, Fuzzy Sets and Systems 63 (1) pp 45– (1994) · doi:10.1016/0165-0114(94)90144-9 |
| [31] | Madar, Genetic programming for the identification of nonlinear input – output models, Industrial and Engineering Chemistry Research 44 pp 3178– (2005) · doi:10.1021/ie049626e |
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