Learning probabilistic relational models: a novel approach.

• Ano de defesa: 2018
• Autor(a) principal: Mormille, Luiz Henrique Barbosa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Bayesian network; Inductive logic programming; Mineração de dados; Modelos para processos estocásticos; Multi-relational data mining; Probabilistic graphical models; Programação lógica
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3141/tde-12122018-091504/

While most statistical learning methods are designed to work with data stored in a single table, many large datasets are stored in relational database systems. Probabilistic Relational Models (PRM) extend Bayesian networks by introducing relations and individuals, thus making it possible to represent information in a relational database. However, learning a PRM from relational data is a more complex task than learning a Bayesian Network from \"flat\" data. The main difficulties that arise while learning a PRM are establishing what are the legal dependency structures, searching for possible structures, and scoring them. This thesis focuses on the development of a novel approach to learn the structure of a PRM, describes a package in the R language to support the learning framework, and applies it to a real, large scale scenario of a city named Atibaia, in the state of São Paulo, Brazil. The research is based on a database combining three different tables, each representing one class in the domain of study. The first table contains 27 attributes from 110,816 citizens of Atibaia. The second table contains 9 attributes from 20,162 companies located in the city. And finally, the third table has 8 attributes from 327 census sectors (small territorial units that comprise the city of Atibaia). The proposed framework is applied to learn a PRM structure and parameters from the database. The model is used to verify if the Social Class of a person can be explained by the location where they live, their neighbors, and the companies nearby. Preliminary experiments have been conducted and a paper published in the 2017 Symposium on Knowledge Discovery, Mining and Learning (KDMiLe). The algorithm performance was further evaluated by extensive experimentation, and a broader study using Serasa Experian data was conducted. Finally, the package in the R language that supports our method was refined along with proper documentation and a tutorial.