Implementation of Feature Selection using Information Gain Algorithm and Discretization with NSL-KDD Intrusion Detection System

Abstract

Feature selection is one of the research on data mining for datasets that have relatively many attributes. Eliminating some attributes that are irrelevant to the label class will be able to improve the performance of the classification algorithm. The Information Gain algorithm is one of the algorithms for searching for features that are irrelevant to the label class. This algorithm uses wrapper techniques to eliminate irrelevant attributes. This research aims to implement feature selection using the Information Gain algorithm against the NSL KDD intrusion detection dataset which has a large number of relative attributes. The dataset of the selected attribute will be performed by a classification algorithm so that an attribute reduction can improve the compute process and improve the accuracy of the algorithm model used.