Handling binary classification problems with a priority class by using Support Vector Machines
| Autor: | Yenny Leal, Cecilio Angulo, Luis Gonzalez-Abril, Haydemar Núñez |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Support vector machines
Structured support vector machine Informàtica::Automàtica i control [Àrees temàtiques de la UPC] Post-processing strategies Computer science business.industry Pattern recognition 02 engineering and technology Support vector machine Binary classification 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Aprenentatge automàtic -- Algorismes Classifier (UML) Software Cost-sensitive SVM |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | © 2017 Elsevier B.V. A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class). That is, the true positive rate (or recall or sensitivity) is prioritized over the accuracy of the overall classifier. Hence, false negative (or Type I) errors receive greater consideration than false positive (Type II) errors during the construction of the model. This post-processing technique tunes the initial bias term once a solution vector is learned by using standard SVM algorithms in two steps: First, a fixed threshold is given as a lower bound for the recall measure; second, the true negative rate (or specificity) is maximized. Experiments, carried out on eleven standard UCI datasets, show that the modified SVM satisfies the aims for which it has been designed. Furthermore, results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered. |
| Databáze: | OpenAIRE |
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