HEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKS | Science Publications

Journal of Computer Science

Mariana D.C. Lima¹  Pedro Ivo R.B.G. Rodrigues¹  Carlos M.C. Jacinto¹  Paulo J. Freitas Filho¹  Silvia M. Nassar¹ 

关键词: Bayesian Network;    Discretization;    Global Optimization;    Genetic Algorithm;    Heuristic;   

DOI  :  10.3844/jcssp.2014.869.878

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In this article we present a discretization using a heuristic that identifies events called peak and valley. Genetic Algorithm was used to identify these events having the minimization of the error between the estimated average for BN and the actual value of the numeric variable output as the objective function. The BN has been modeled from a database of Bit’s Rate of Penetration of the Brazilian pre-salt layer with 5 numerical variables and one categorical variable, using the proposed discretization and the division of the data by the quartiles. The results show that the proposed heuristic discretization has higher accuracy than the quartiles discretization.

【 授权许可】

Unknown