A Fuzzy Piecewise Regression Model with Change-point Detection
博士 === 國立交通大學 === 資訊管理所 === 87 === The possibilistic regression analysis proposed by Tanaka and Ishibuchi, which is extremely sensitive to outliers, may not able to find feasible solution. Besides, when they use linear programming in possibilistic regression analysis, some coefficients are limited t...
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| Format: | Others |
| Language: | en_US |
| Published: |
1999
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| Online Access: | http://ndltd.ncl.edu.tw/handle/98190298945413986532 |
| Summary: | 博士 === 國立交通大學 === 資訊管理所 === 87 === The possibilistic regression analysis proposed by Tanaka and Ishibuchi, which is extremely sensitive to outliers, may not able to find feasible solution. Besides, when they use linear programming in possibilistic regression analysis, some coefficients are limited to be crisp because of the characteristic of linear programming. To overcome large variation problem, we propose fuzzy piecewise regression method. Our method can also treat the problem with crisp coefficients by utilizing quadratic programming approach. The proposed fuzzy piecewise regression method has two advantages: (a) It can detect the positions of change-points and can estimate the fuzzy piecewise regression model simultaneously; (b) It can deal with outliers by automatically segmenting the data.
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