Earticle

초록

영어

In this paper, we propose a new boosting algorithm for distributed databases. The main idea of the proposed method is to utilize the parallelism of the distributed databases to build an ensemble of classifiers. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the global classifier, which is then a classifier in the ensemble. This global classifier is also used by each distributed site to compute required information for the next round. By epeating this process, an ensemble of classifiers, which is almost identical to the one built on the whole data, will be produced from the distributed databases. The experiments were erformed on 5 different datasets from the UCI repository [9]. The experimental results show that the accuracy of the proposed algorithm is almost equal to or higher than the accuracy when pplying boosting algorithm to the whole database.

목차

Abstract
 1. Introduction
 2. Boosting algorithm
 3. Distributed boosting algorithm
  3.1. Training phase
  3.2. Classification phase
  3.3. Base classifier
 4. Performance evaluation
 5. Experiments
  5.1. Datasets
  5.2. Performance comparison
 6. Conclusion
 References

저자

• Nguyen Thi Van Uyen [ Artificial Intelligent Lab., KyungHee University ]
• Seung Gwan Lee [ Artificial Intelligent Lab., KyungHee University ]
• TaeChoong Chung [ Artificial Intelligent Lab., KyungHee University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605