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Real-time fuzzy regression analysis: A convex hull approach

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  • Ramli, Azizul Azhar
  • Watada, Junzo
  • Pedrycz, Witold

Abstract

In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression.

Suggested Citation

  • Ramli, Azizul Azhar & Watada, Junzo & Pedrycz, Witold, 2011. "Real-time fuzzy regression analysis: A convex hull approach," European Journal of Operational Research, Elsevier, vol. 210(3), pages 606-617, May.
    • Handle: RePEc:eee:ejores:v:210:y:2011:i:3:p:606-617
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    1. Guo, Peijun & Tanaka, Hideo, 2010. "Decision making with interval probabilities," European Journal of Operational Research, Elsevier, vol. 203(2), pages 444-454, June.
    2. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
    3. Narula, Subhash C. & Wellington, John F., 2007. "Multiple criteria linear regression," European Journal of Operational Research, Elsevier, vol. 181(2), pages 767-772, September.
    4. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    5. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    6. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    7. James W. Taylor, 2008. "A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center," Management Science, INFORMS, vol. 54(2), pages 253-265, February.
    8. Kao, Chiang & Chyu, Chin-Lu, 2003. "Least-squares estimates in fuzzy regression analysis," European Journal of Operational Research, Elsevier, vol. 148(2), pages 426-435, July.
    9. Hsu, Bi-Min & Shu, Ming-Hung, 2008. "Fuzzy inference to assess manufacturing process capability with imprecise data," European Journal of Operational Research, Elsevier, vol. 186(2), pages 652-670, April.
    10. Tanaka, Hideo & Guo, Peijun, 1999. "Portfolio selection based on upper and lower exponential possibility distributions," European Journal of Operational Research, Elsevier, vol. 114(1), pages 115-126, April.
    11. Aznar, Jeronimo & Guijarro, Francisco, 2007. "Estimating regression parameters with imprecise input data in an appraisal context," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1896-1907, February.
    12. Pekka Korhonen & Mikko Syrjänen, 2004. "Resource Allocation Based on Efficiency Analysis," Management Science, INFORMS, vol. 50(8), pages 1134-1144, August.
    13. He, Yan-Qun & Chan, Lai-Kow & Wu, Ming-Lu, 2007. "Balancing productivity and consumer satisfaction for profitability: Statistical and fuzzy regression analysis," European Journal of Operational Research, Elsevier, vol. 176(1), pages 252-263, January.
    14. Wu, Chien-Wei, 2009. "Decision-making in testing process performance with fuzzy data," European Journal of Operational Research, Elsevier, vol. 193(2), pages 499-509, March.
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    2. Attah-Boakye, Rexford & Adams, Kweku & Hernandez-Perdomo, Elvis & Yu, Honglan & Johansson, Jeaneth, 2023. "Resource re-orchestration and firm survival in crisis periods: The role of business models of technology MNEs during COVID-19," Technovation, Elsevier, vol. 125(C).

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