关键词: 特征提取, 最大间隔, 多层感知器, 集成算法, KDDCUP99数据集, 入侵检测

Abstract: The idea of ensemble learning is to employ multiple learners and combine their predictions. The typical methods of combining multiple models such as bagging, boosting, stacking error correcting output codes, voting, mixtures of experts, stacked generalization and cascading. Though a considerable effort has been put into developing statistical models and algorithmic strategies for classification, the accurate of the classification has been proven to be very challenging. A novel ensemble algorithm, ReinforcedEnsemble is proposed. ReinforcedEnsemble ensemble algorithm consists of two parts, ReinforcedEnsemble feature extraction algorithm and ReinforcedEnsemble base classifier. The performance between ReinforcedEnsemble and other ensemble algorithm in the experiments is compared. The experimental results show that the proposed algorithm is optimal in a number of indicators.

Key words: feature extraction, maximum margin, multilayer perceptron, assemble algorithm, KDDCUP99 data set, intrusion detection