Earticle

초록

영어

Bayesian network is a directed acyclic graph. Existing Bayesian network learning approaches based on search & scoring usually work with a heuristic search for finding the highest scoring structure. This paper describes a new data mining algorithm to learn Bayesian networks structures based on an immune binary particle swarm optimization (IB-PSO) method and the Minimum Description Length (MDL) principle. IBPSO is proposed by combining the immune theory in biology with particle swarm optimization (PSO). It constructs an immune operator accomplished by two steps, vaccination and immune selection. The purpose of adding immune operator is to prevent and overcome premature convergence. Experiments show that IBPSO not only improves the quality of the solutions, but also reduces the time cost.

목차

Abstract
 1. Introduction
 2. Bayesian networks and MDL metric
  2.1. Bayesian networks
  2.2. The MDL metric
 3. Particle Swarm Optimization
 4. Immune Binary Particle Swarm Optimization Method
 5. Experiments
 6. Conclusions
 References

키워드

저자

• Xiao-Lin Li [ School of Business, Nanjing University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605