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
Tabu search model selection for svm
TL;DR: A model selection method based on tabu search is proposed to build a fast and efficient support vector machines classifier to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together.
Training feedforward neural network via multiobjective optimization model using non-smooth L1/2 regularization
TL;DR: It is shown empirically that the proposed method is capable of reducing the neural networks topology and improved generalization performance, in addition to a good classification rate compared to different methods.
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Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait
TL;DR: An efficient approach is presented that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports, and shows that the main differences are characterized by balance and joints excursion in the sagittal plane.
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•Proceedings Article
Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach.
Fernando Mendes,João Duarte,Armando Vieira,António Gaspar-Cunha +3 more
- 01 Jan 2009
TL;DR: It is shown that MOEA is an efficient feature selection approach in the problem of bankruptcy prediction and can provide useful information for decision makers in characterizing the financial health of a company.
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•Proceedings Article
Multi-objective multi-label classification
Chuan Shi,Xiangnan Kong,Philip S. Yu,Bai Wang +3 more
- 01 Dec 2012
TL;DR: The Moml algorithm finds a set of non-dominated solutions which are optimal according to the different tradeoffs of the multiple objectives, which helps to provide more meaningful classification results in different application scenarios.
15
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