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Improving SVM classification on imbalanced datasets by introducing a new bias.

 

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Opened Access Improving SVM classification on imbalanced datasets by introducing a new bias.
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Author: Nuñéz, Haydemar
González-Abril, Luis
Angulo, Cecilio
Department: Universidad de Sevilla. Departamento de Economía Aplicada I
Date: 2017
Document type: Article
Abstract: Support Vector Machine (SVM) learning from imbalanced datasets, as well as most learning machines, can show poor performance on the minority class because SVMs were designed to induce a model based on the overall error. To improve their performance ...
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Cite: Nuñéz, H., González-Abril, L. y Angulo, C. (2017). Improving SVM classification on imbalanced datasets by introducing a new bias.. Journal of Classification, 34 (3), 427-443.
Size: 338.0Kb
Format: PDF

URI: https://hdl.handle.net/11441/86489

DOI: 10.1007/s00357-017-9242-x

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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