Abstract
Fuzzy linear regression is an important tool to find the linear inexact relationship between uncertain data. We then propose a hybrid optimization method based on tabu search and harmony search as a potential way of solving fuzzy linear regression. The proposed method aims at finding a model without considering any mathematical constraints while reducing the error of the regression’s model in comparison to other methods. The experimental comparison of the results for two classes of crisp input-fuzzy output and fuzzy input-fuzzy output data sets shows the superiority of the method over the existing ones.
This work has been supported in part under Australian Research Councils Linkage Projects Funding Scheme (project number LP0561985) and Macquarie University’s International Travel Grant Scheme.
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Mashinchi, M.H., Orgun, M.A., Mashinchi, M. (2009). Solving Fuzzy Linear Regression with Hybrid Optimization. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_37
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DOI: https://doi.org/10.1007/978-3-642-10684-2_37
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