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

被引：15
|
作者：
Li, Xiao-Lin [1 ]
He, Xiang-Dong [2 ]
机构：
[1] Nanjing Univ, Sch Management, Nanjing 210093, Jiangsu, Peoples R China
[2] Nanjing Univ, Network & Informat Ctr, Nanjing 210093, Jiangsu, Peoples R China
来源：
INFORMATION SCIENCES | 2014年 / 283卷
基金：
美国国家科学基金会;
关键词：
Machine learning; Relational learning; Probabilistic relational model; Immune theory; Particle swarm optimization; CLASSIFICATION; PSO;
D O I：
10.1016/j.ins.2014.04.058
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：258 / 266
页数：9
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