Evaluation of feature selection techniques on network traffic for comparing model accuracy
by Prabhjot Kaur; Amit Awasthi; Anchit Bijalwan
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 3, 2021

Abstract: The accuracy and performance of any machine learning model are highly dependent on the number of qualitative features taken into consideration while training the model. The selection of qualitative features depends on the considerate choice of feature selection technique. In this study, feature selection is performed using different techniques such as information gain, Gini decrease, Chi² and FCBF on the same dataset, and subsequently, the accuracy has been measured. The results showed that the FCBF method has dramatically reduced the number of features and moderated the accuracy compared with other feature selection methods.

Online publication date: Tue, 15-Jun-2021

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