Browsing by Author "Yu, JR"

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A fuzzy multiple objective programming to dea with imprecise data
(2004-10-01)
Yu, JR
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Tzeng, YC
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Tzeng, GH
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Yu, TY
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Sheu, HJ
A fuzzy multiple objective programming approach to data envelopment analysis (DEA) of imprecise data is proposed in this paper. The problems involving a mixture of imprecise and exact data for all decision making units (DMUs) could be resolved and the discriminating power of imprecise DEA (IDEA) is enhanced. Although Cooper et al. have developed IDEA to overcome the issues of imprecise data, the discriminating power is not satisfactory since too many efficient DMUs are derived. Chiang and Tzeng's approach using fuzzy multiple objective programming techniques is adopted to enhance the discriminating power of IDEA. The same data set of Cooper et al. is employed to illustrate the merit of our approach.
A general piecewise necessity regression analysis based on linear programming
(1999-08-01)
Yu, JR
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Tzeng, GH
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Li, HL
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運輸與物流管理系註：原交通所+運管所
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資訊管理與財務金融系註：原資管所+財金所
In possibilistic regression analysis proposed by Tanaka and Ishibuchi (1992), linear programming (LP) formulation of necessity analysis has no feasible solution under the enormous variation of the given data. This work proposes a general piecewise necessity regression analysis based on LP rather than a non-linear interval model that they recommended to obtain the necessity area of the given data. In addition to maintaining a linear property, the proposed method prevents the necessity analysis from having no feasible solution. The problematic univariate example and a multivariate example with respect to different number of change-points are demonstrated by the general piecewise necessity regression. The proposed method characteristic is that, according to data distribution, practitioners can specify the number and the positions of change-points. The proposed method maintains the linear interval model and the order of necessity regression function does not need to be determined. (C) 1999 Elsevier Science B.V. All rights reserved.
General fuzzy piecewise regression analysis with automatic change-point detection
(2001-04-16)
Yu, JR
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Tzeng, GH
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Li, HL
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管理學院
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運輸與物流管理系註：原交通所+運管所
Yu et al. (Fuzzy Sets and Systems 105 (1999) 429) performed general piecewise necessity regression analysis based on linear programming (LP) to obtain the necessity area. Their method is the same as that according to data distribution, even if the data are irregular, practitioners must specify the number and the positions of change-points. However, as the sample size increases, the number of change-points increases and the piecewise linear interval model also becomes complex. Therefore, this work devises general fuzzy piecewise regression analysis with automatic change-point detection to simultaneously obtain the fuzzy regression model and the positions of change-points. Fuzzy piecewise possibility and necessity regression models are employed when the function behaves differently in different parts of the range of crisp input variables. As stated, the above problem can be formulated as a mixed-integer programming problem. The proposed fuzzy piecewise regression method has three advantages: (a) Previously specifying the number of change-points, then the positions of change-points and the fuzzy piecewise regression model are obtained simultaneously. (b) It is more robust than conventional fuzzy regression. The conventional regression is sensitive to outliers. In contrast, utilizing piecewise concept, the proposed method can deal with outliers by automatically segmenting the data. (c) By employing the mixed integer programming, the solution is the global optimal rather than local optimal solution. For illustrating more detail, two numerical examples are shown in this paper. By using the proposed method, the fuzzy piecewise regression model with detecting change-points can be derived simultaneously. (C) 2001 Elsevier Science B.V. All rights reserved.
Interval piecewise regression model with automatic change-point detection by quadratic programming
(2005-06-01)
Yu, JR
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Tzeng, GH
;
Li, HL
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科技管理研究所
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Institute of Management of Technology
To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.