Support Vector Machine as Feature Selection Method in Classifier Ensembles

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Jasmina D. Novakovic 1,*

1. Belgrade Business School, Belgrade, 11000, Serbia

* Corresponding author.


Received: 10 Jan. 2014 / Revised: 12 Feb. 2014 / Accepted: 2 Mar. 2014 / Published: 8 Apr. 2014

Index Terms

Classification accuracy, feature selection, classifier ensembles, machine learning, Support Vector Machine


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.

Cite This Paper

Jasmina Đ. Novakovic, "Support Vector Machine as Feature Selection Method in Classifier Ensembles", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.4, pp.1-8, 2014. DOI:10.5815/ijmecs.2014.04.01


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