CAJ | 학술논문

针对入侵检测的标记数据难以获得的问题,提出一种基于集成学习的Self-training方法--正则化Self-training.该方法结合主动学习和正则化理论,利用无标记数据对已有的分类器(该分类器对分类模式已学习得很好)作进一步的改进.对三种主要的集成学习方法在不同标记数据比例下进行对比实验,实验结果表明:借助大量无标记数据可以改善组合分类器的分类边界,算法能显著地降低结果分类器的错误率.
침대입침검측적표기수거난이획득적문제,제출일충기우집성학습적Self-training방법--정칙화Self-training.해방법결합주동학습화정칙화이론,이용무표기수거대이유적분류기(해분류기대분류모식이학습득흔호)작진일보적개진.대삼충주요적집성학습방법재불동표기수거비례하진행대비실험,실험결과표명:차조대량무표기수거가이개선조합분류기적분류변계,산법능현저지강저결과분류기적착오솔.
Regularization self-training is a new method based on ensemble learning. It can solve the problem of insufficient labeled training samples in intrusion detection. The proposed algorithm combined active learning and regularization theory, and utilized unlabeled data to improve the existing classifiers. The experiments were running on three main ensemble learning algorithms under different unlabeled rate. The results prove that the proposed method can improve the boundary of the ensemble classifiers, and reduce the error rate with the help of large amounts of unlabeled data.