A Feature Subset Selection Method Based on the Combination of PCA and Improved GA
Yinghao Li, Kai Shi, Fuqiang Qiao, Hongyu Luo · 2020
Nowadays, intrusion detection data has redundant features, which leads to unsatisfactory detection results. This paper proposes a feature selection method based on the combination of principal component analysis (PCA) and improved Genetic algorithm(GA). First, PCA is used to reduce the dimensionality of the intrusion data, and the cosine similarity is used to calculate angles between the principal component and the original feature; the feature with high cosine similarity to the principal component is selected and the genetic algorithm is input, and the improved inter-group crossover strategy is adopted to reduce the probability of entering the local optimum. The fitness function of the genetic algorithm is the detection rate, and the optimal feature subset is selected as the intrusion detection data feature. Experimental results show that this method can improve the efficiency of intrusion detection and improve the classification ability of the classifier.