Support Vector Machine as Feature Selection Method in Classifier Ensembles
Автор: Jasmina Đ. Novakovic
Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs
Статья в выпуске: 4 vol.6, 2014 года.
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In this paper, we suggest classifier ensembles that can incorporate Support Vector Machine (SVM) as feature selection method into classifier ensembles models. Consequences of choosing different number of features are monitored. Also, the goal of this research is to present and compare different algorithmic approaches for constructing and evaluating systems that learn from experience to make the decisions and predictions and minimize the expected number or proportion of mistakes. Experimental results demonstrate the effectiveness of selecting features with SVM in various types of classifier ensembles.
Classification accuracy, feature selection, classifier ensembles, machine learning, Support Vector Machine
Короткий адрес: https://sciup.org/15014641
IDR: 15014641
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