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On the Use of Adaptive Weights for the F-Norm Support Vector Machine
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 Title & Authors
On the Use of Adaptive Weights for the F-Norm Support Vector Machine
Bang, Sung-Wan; Jhun, Myoung-Shic;
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When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The -norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the -norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive -norm (-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise -norm penalty. The -norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the -norm SVM. The simulation studies show that the proposed -norm SVM improves upon the -norm SVM in terms of classification accuracy and factor selection performance.
Adaptive weight;-norm penalty;factor selection;feature selection;support vector machine;
 Cited by
그룹변수를 포함하는 불균형 자료의 분류분석을 위한 서포트 벡터 머신,김은경;전명식;방성완;

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