Earticle

초록

영어

Multi-observation areas based ensemble learning models are proposed for general fatigue performance identification. It is believed that although the expression of fatigue is not obvious. If the observation areas are concerned, the performance of fatigue will be relatively concentrated and the law of expression changes will be much clearer. Another advantage of feature extraction from the observation areas is that it can greatly reduce the interference caused by redundant information in the face when learning classifiers. For each observation area, a C4.5 base classification model is built. Each base model is equivalent to an independent decision maker. But due to its limited capacity, it may not be able to give accurate decisions. However, if the information provided by each decision maker is combined together, it will form comprehensive evidence. Driver status classification results given by ensemble learning approach are more accurate and stable.

목차

Abstract
 1. Introduction
 2. Multi-level Information Acquisition
 3. Feature Evaluation Method Based On Rough Set
 4. Pattern Classifier Integration
  4.1 C4.5 Decision Trees
  4.2 The Base Classifier Ensemble Learning
 5. Experiment Design and Discussion
 6. Conclusion
 References

키워드

저자

• Ye Sun [ College of computer, Changchun Normal University, Jilin 130032, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605