Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

• Ano de defesa: 2006
• Autor(a) principal: Pifer, Aderson Cleber
• Orientador(a): Oliveira, Luiz Affonso Henderson Guedes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Elétrica
• Departamento: Automação e Sistemas; Engenharia de Computação; Telecomunicações
• País: BR
• Palavras-chave em Português: Redes bayesianas; Métricas; Aprendizagem estrutural; ALARM; ASIA; Redes de crença; K-2; MDL; BDe
• Palavras-chave em Inglês: Bayesian networks; Score metric; Structural learning; ALARM; ASIA; Belief networks; K-2; MDL, BDe
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/15513

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures