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Your search keyword '"Özmen, Ayşe"' showing total 11 results
Start Over Author "Özmen, Ayşe" Topic robust optimization
11 results on '"Özmen, Ayşe"'

9. Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty.

Author

Özmen, Ayşe, Kropat, Erik, and Weber, Gerhard-Wilhelm

Subjects
*MATHEMATICAL optimization, *SPLINE theory, *GENE regulatory networks, *UNCERTAINTY (Information theory), *ROBUST control, *APPROXIMATION theory
Abstract

In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target–environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model’s reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the target–environment interaction, based on the expression values of all targets and environmental factors. [ABSTRACT FROM AUTHOR]

Published
2017
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10. Robust conic generalized partial linear models using RCMARS method - A robustification of CGPLM.

Author

Özmen, Ayşe and Weber, Gerhard Wilhelm

Subjects
*ROBUST optimization, *QUADRATIC programming, *LINEAR statistical models, *LOGISTIC regression analysis, *ALGORITHMS
Abstract

GPLM is a combination of two different regression models each of which is used to apply on different parts of the data set. It is also adequate to high dimensional, non-normal and nonlinear data sets having the flexibility to reflect all anomalies effectively. In our previous study, Conic GPLM (CGPLM) was introduced using CMARS and Logistic Regression. According to a comparison with CMARS, CGPLM gives better results. In this study, we include the existence of uncertainty in the future scenarios into CMARS and linear/logit regression part in CGPLM and robustify it with robust optimization which is dealt with data uncertainty. Moreover, we apply RCGPLM on a small data set as a numerical experience from the financial sector. [ABSTRACT FROM AUTHOR]

Published
2012
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11. RCMARS: Robustification of CMARS with different scenarios under polyhedral uncertainty set

Author

Özmen, Ayşe, Weber, Gerhard Wilhelm, Batmaz, İnci, and Kropat, Erik

Subjects
*POLYHEDRA, *ROBUST optimization, *SET theory, *MATHEMATICAL variables, *ROBUST control, *REGRESSION analysis, *ALGORITHMS
Abstract

Abstract: Our recently developed CMARS is powerful in handling complex and heterogeneous data. We include into CMARS the existence of uncertainty about the scenarios. Indeed, data include noise in both output and input variables. Therefore, solutions of the optimization problem may reveal a remarkable sensitivity to perturbations in the parameters of the problem. The data uncertainty results in uncertain constraints and objective function. To overcome this difficulty, we refine our CMARS algorithm by a robust optimization technique proposed to cope with data uncertainty. In our previous study, we present the new robust CMARS (RCMARS) in theory and method and illustrate it with a numerical example. In this study, we present RCMARS results with different uncertainty scenarios for our numerical example. [Copyright &y& Elsevier]

Published
2011
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