计算机工程与应用2012，Vol.48Issue(34)：17-22,6.DOI:10.3778/j.issn.1002-8331.1203-0682
集成学习算法的研究与应用
Study of ensemble algorithm and its application
侯勇 1郑雪峰2
作者信息
- 1. 北京科技大学计算机与通信工程学院,北京100083
- 2. 山东经贸职业学院科学与人文学院,山东潍坊261011
- 折叠
摘要
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.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Key words
feature extraction/ maximum margin/ multilayer perceptron/ assemble algorithm/ KDDCUP99 data set/ intrusion detection分类
信息技术与安全科学引用本文复制引用
侯勇,郑雪峰..集成学习算法的研究与应用[J].计算机工程与应用,2012,48(34):17-22,6.
