Earticle

초록

영어

Classification is a data mining technique widely used in critical domains like financial risk analysis, biology, communication network management, etc. Classification accuracy and learning from distributed datasets are the most challenging topics in the field of supervised learning. In this paper, we first briefly review the background of parallel and distributed classification algorithms and then propose a novel approach for classification in distributed large datasets. This approach is based on code migration instead of data migration. Extensive experimental results using a popular benchmark test suite show the effectiveness of this approach in term of accuracy. These results show also that the proposed method improved slightly classification accuracy over standard methods.

목차

Abstract
 1. Introduction
 2. Parallel and Distributed Classification Algorithms
  2.1. Parallel Decision Trees
  2.2. Parallel Artificial Neural Networks
  2.3. Parallel Boosting
  2.4. Meta-learning
 3. Our Proposed Method
  3.1. Position of the Problem
  3.2. Methodology
  3.3. Distributed Learning Algorithm
  3.4. Reduction of Communication Cost
 4. Experimental Evaluation
 5. Conclusion
 References

키워드

저자

• Moez Ben Haj Hmida [ Department of Computer Science, Faculty of Sciences of Tunis, Tunisia ]
• Yahya Slimani [ Department of Computer Science, Faculty of Sciences of Tunis, Tunisia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605