A Stable Stochastic Optimization Algorithm for Triangulation of Bayesian Networks

被引：2

作者：

Dong, Xuchu ^{[1
]}

Ouyang, Dantong ^{[1
]}

Ye, Yuxin ^{[1
]}

Feng, Shasha ^{[1
]}

Yu, Haihong ^{[1
]}

机构：

[1] Jilin Univ, Dept Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China

来源：

THIRD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING: WKDD 2010, PROCEEDINGS | 2010年

关键词：

Bayesian networks; clique tree; heuristics; genetic algorithm; ant colony optimization;

D O I：

10.1109/WKDD.2010.84

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative benchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stability to various problems.

引用

页码：466 / 469

页数：4

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