Open AccessProceedings Article
Multi-objective programming in SVMs
Jinbo Bi
- 21 Aug 2003
- pp 35-42
TL;DR: A feature selection approach based on the MOP framework is developed and demonstrated its effectiveness on hand-written digit data.
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Abstract: We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multi-objective program by setting two competing goals to minimize the empirical risk and minimize the model capacity. Distinct approaches to solving the MOP introduce various SVM formulations. The proposed framework enables a more effective minimization of the VC bound on the generalization risk. We develop a feature selection approach based on the MOP framework and demonstrate its effectiveness on hand-written digit data.
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Citations
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
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TL;DR: An overview of the existing research on multiobjective machine learning, focusing on supervised learning is provided, and a number of case studies are provided to illustrate the major benefits of the Pareto-based approach to machine learning.
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Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach.
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Multiobjective Support Vector Machines: Handling Class Imbalance With Pareto Optimality
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Feature Selection Using Multi-Objective Evolutionary Algorithms: Application to Cardiac SPECT Diagnosis
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TL;DR: The results obtained allow one to conclude that MOEA is an efficient feature selection approach and the best results were obtained when the accuracy, the errors and the classifiers parameters are optimized simultaneously.
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