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Bayesian network quantization method and structural learning

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Rafael Rodrigues Mendes Ribeiro
Total Authors: 1
Document type: Doctoral Thesis
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD)
Defense date:
Examining board members:
Carlos Dias Maciel; Cassio Polpo de Campos; André Carlos Ponce de Leon Ferreira de Carvalho; Eduardo Antonio Barros da Silva; Ricardo Zorzetto Nicoliello Vencio
Advisor: Carlos Dias Maciel

Bayesian Networks (BNs) are versatile models for capturing complex relationships, widely applied in diverse fields. This study focuses on discrete variable BNs. Modeling quality depends on adequate data volume, especially for constructing conditional probability tables (CPTs). The quantity of required data varies with the chosen BN Directed Acyclic Graph (DAG). Structural learning of the BN involves an NP-hard problem with a super-exponential DAG search space. This thesis proposes investigating multi-objective optimization in BN structural learning (BNSL) to balance conflicting criteria. The approach utilizes Pareto sets and multi-objective Genetic Algorithms (GAs). To perform BNSL, a parallel GA with automatic parameter adjustment is developed, called Adaptive Genetic Algorithm with Varying Population Size (AGAVaPS). This proposed algorithm is thoroughly tested on different applications and BNSL. AGAVaPS is found to be a good algorithm to be used in BNSL, performing better than HillClimbing and Tabu Search for some of the metrics measured. The study also explores the impact of data quantization on the BNSL search space. It also introduces a quantization method called CPT Limit-Based Quantization (CLBQ) that balances model quality, data fidelity, and structure score. The effectiveness of this method is tested and its capability of being used in search and score BNSL is investigated. CLBQ is found to be a good quantization algorithm, choosing quantization that has a good mean squared error and modeling well the variables\' distributions. Also, CLBQ is suitable to be used on BNSL. (AU)

FAPESP's process: 18/23139-8 - Structural learning of dynamic Bayesian Networks using multiobjective parallel evolutionary algorithm
Grantee:Rafael Rodrigues Mendes Ribeiro
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)