A hybrid particle swarm optimization method for structure learning of probabilistic relational models

作者：Li Xiao Lin^*; He Xiang Dong

来源：Information Sciences, 2014, 283: 258-266.

DOI：10.1016/j.ins.2014.04.058

• Machine learning
• Relational learning
• Probabilistic relational model
• Immune theory
• Particle swarm optimization

Probabilistic relational models (PRMs) extend the Bayesian network representation to incorporate a much richer relational structure. Existing probabilistic relational model (PRM) learning approaches based on search and scoring usually perform a heuristic search for the highest scoring structure. In this paper, we proposes the maximum likelihood tree based immune binary particle swarm optimization (MLT-IBPSO) method to learn structures of PRMs from relational data. First, a maximum likelihood tree (MLT) is generated from the data sample, and a population is created according to the MLT. Then, immune theory is combined with particle swarm optimization (PSO) for searching the structures. As a result, the probabilistic structure is learned based on the proposed method. Experiments show that the MLT-IBPSO method can learn structures from relational data effectively.

• 出版日期2014-11-1
• 单位南京大学