A Hybrid Feature Selection Algorithm

Abstract

Feature selection algorithm in intrusion detection, data mining and pattern recognition plays a crucial role, it deletes unrelated and redundant features of the original data set to the optimal feature subset which are applied to some evaluation criteria. Due to the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm, in the paper, we put forward a hybrid feature selection algorithm towards efficient intrusion detection, this algorithm chooses the optimal feature subset by combining the correlation algorithm and redundancy algorithm. Experimental results show that the algorithm shows almost and even better than the traditional feature selection algorithm on the different classifiers.