Using support vector machines to learn the efficient set in multiple objective discrete optimization

DOI10.1016/j.ejor.2007.09.002zbMath1157.90013OpenAlexW2080714465MaRDI QIDQ958095

Serpil Sayın, Haldun Aytug

Publication date: 2 December 2008

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2007.09.002

zbMATH Keywords

support vector machinesmachine learningefficient setmultiple objective optimization

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29)

Related Items (6)

Dynamic programming algorithms for the bi-objective integer knapsack problem ⋮ Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system ⋮ Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations ⋮ Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective ⋮ Fourier Analysis and Swarm Intelligence for Stochastic Optimization of Discrete Functions ⋮ A reduction dynamic programming algorithm for the bi-objective integer knapsack problem

Uses Software

• LIBSVM

Cites Work

• Unnamed Item
• Unnamed Item
• Two-phases method and branch and bound procedures to solve the bi-objective knapsack problem
• Solving bicriteria 0--1 knapsack problems using a labeling algorithm.
• Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming
• Tabu search based procedure for solving the 0-1 multiobjective knapsack problem: The two objectives case
• A survey and annotated bibliography of multiobjective combinatorial optimization
• Performance of the MOSA method for the bicriteria assignment problem
• Risk Minimization and Minimum Description for Linear Discriminant Functions
• The Multiobjective Discrete Optimization Problem: A Weighted Min-Max Two-Stage Optimization Approach and a Bicriteria Algorithm
• Genetic Algorithms and Random Keys for Sequencing and Optimization
• Knowledge discovery in a direct marketing case using least squares support vector machines
• Text categorization with support vector machines. How to represent texts in input space?

This page was built for publication: Using support vector machines to learn the efficient set in multiple objective discrete optimization