Journal
INFORMATION SCIENCES
Volume 283, Issue -, Pages 258-266Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.04.058
Keywords
Machine learning; Relational learning; Probabilistic relational model; Immune theory; Particle swarm optimization
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Funding
- National Science Foundation of China [60803055]
- China MOE Research Project of Humanities and Social Science [08JC630041]
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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. (C) 2014 Elsevier Inc. All rights reserved.
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