Earticle

초록

영어

Mean Field Genetic Algorithm (MGA) is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). It combines benefit of rapid convergence property of MFA and effective genetic operations of SGA. This paper presents an approach for building a multi-classifier system in a MGA-based inductive learning environment. Multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The general classifier performs regular classification task. The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decision-making process or not. The paper discusses our approach in details and presents some empirical results that show the improvement we can achieve with our approach.

목차

Abstract
 1. Introduction
 2. Multi-classifier System
  2.1. Learning Classification Rules
  2.2. Building a Multi-classifier System
 3. Mean Field Genetic Algorithm
  3.1. Simulated Annealing-like Genetic Algorithm (SGA)
  3.2. Mean Field Annealing (MFA)
  3.3. MGA Hybrid Algorithm
 4. Experiments
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Chuleui Hong [ Department of Computer Science, Sangmyung University, Seoul, Korea ]
• Yeongjoon Kim [ Department of Computer Science, Sangmyung University, Seoul, Korea ] Corresponding author.

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