Model building using bi-level optimization

Authors:
G. K. Saharidis;I. P. Androulakis;M. G. Ierapetritou
Affiliations:
Center for Advanced Infrastructure and Transportation, Rutgers, The State University of New Jersey, Piscataway, USA;Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, USA;Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, USA
Venue:
Journal of Global Optimization
Year:
2011

Citing 12
Cited 0

Algorithms for solving the mixed integer two-level linear programming problem

Computers and Operations Research
The mixed integer linear bilevel programming problem

Operations Research
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Robust regression methods for computer vision: a review

International Journal of Computer Vision
A Multivariate Partition Approach to Optimization Problems

Cybernetics and Systems Analysis
Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

Bioinformatics
Parametric global optimisation for bilevel programming

Journal of Global Optimization
A review of feature selection techniques in bioinformatics

Bioinformatics
A mixed-integer optimization framework for the synthesis and analysis of regulatory networks

Journal of Global Optimization
Resolution method for mixed integer bi-level linear problems based on decomposition technique

Journal of Global Optimization
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Inheritable genetic algorithm for biobjective 0/1 combinatorial optimization problems and its applications

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many problems from different disciplines such as engineering, physics, medicine, and biology, a series of experimental data is used in order to generate a model that can describe a system with minimum noise. The procedure for building a model provides a description of the behavior of the system under study and can be used to give a prediction for the future. Herein a novel hierarchical bi-level implementation of the cross validation method is presented. In this bi-level schema, the leader optimization problem builds (training) the model and the follower checks (testing) the developed model. The problem of synthesis and analysis of regulatory networks is used to compare the classical cross validation method to the proposed methodology referred to as bi-level cross validation. In all the examples considered, the bi-level cross validation results in a better model compared with the classical cross validation approach.