Approximate Reducts-based Ensemble Learning Algorithm and Its Application in Intrusion Detection

To obtain diverse base learners for construct ensemble learner, the issue of ensemble learning was considered from the perspective of partitioning the attribute space. Through rough set theory, the concept of approximate reduct was defined, and further an approximate reducts-based ensemble learning algorithm was proposed. The method could partition the attribute space of data set into multiple subspaces, and the base learners trained on data sets corresponding to different subspaces had large diversity, which guarantee that the ensemble learner has strong generalization performance. To verify the effectiveness of the algorithm, it was applied to network intrusion detection. Experimental results on the KDD CUP 99 data set demonstrate that compared with the traditional ensemble learning algorithms, the proposed method has higher detection rate and lower computational cost, which is more suitable for the detection of intrusions from the massive and high-dimensional network data.