Earticle

초록

영어

In recent years, semi-supervised learning has been a hot research topic in machine learn-ing area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each exam-ple is represented by two or more redundantly sufficient sets of features (views) and addi-tionally these views are independent given the class. To improve the performance and ap-plicability of co-training, ensemble learning, such as bagging and random subspace has been used along with co-training. In this work, we propose to use the rough set based ensem-ble learning method with co-training algorithm (RSCO). Inherited the inherent characteris-tics of rough set, ensemble learning is expected to meet both the diversity and accuracy re-quirement. Finally experimental results on the UCI data sets demonstrate the promising per-formance of RSCO.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Ensemble Learning based on Rough Set
  3.1. Preliminary Knowledge on Rough Set
  3.2. Ensemble Learning with Reducts
 4. Experiments
 5. Conclusions and Future Works
 Acknowledgements
 References

키워드

저자

• Donghai Guan [ College of Automation, Harbin Engineering Univ ]
• Weiwei Yuan [ College of Computer Science & Technology, Harbin Engineering Univ ] the corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605