APLICAÇÃO DE TÉCNICAS DE APRENDIZADO DE MÁQUINA PARA CLASSIFICAÇÃO DE DEPÓSITOS MINERAIS BASEADA EM MODELO TEOR-TONELAGEM

• Ano de defesa: 2010
• Autor(a) principal: Rocha, Jocielma Jerusa Leal
• Orientador(a): PAIVA, Anselmo Cardoso de
• Banca de defesa: Abdelouahab, Zair
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: Classificação de Depósitos Minerais; Modelo Teor-Tonelagem; Aprendizado de Máquina Supervisionado; Combinação de Classificadores
• Palavras-chave em Inglês: Mineral Deposits Classification; Grade-Tonnage Model; Supervised Machine Learning; Combination of Classifiers
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::SISTEMAS DE INFORMACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/444

Classification of mineral deposits into types is traditionally done by experts. Since there are reasons to believe that computational techniques can aid this classification process and make it less subjective, the research and investigation of different methods of clustering and classification to this domain may be appropriate. The way followed by researches in this domain has directed for the use of information available in large public databases and the application of supervised machine learning techniques. This work uses information from mineral deposits available in grade-tonnage models published in the literature to conduct research about the suitability of these three techniques: Decision Tree, Multilayer Perceptron Network and Probabilistic Neural Network. Altogether, 1,861 mineral deposits of 18 types are used. The types refer to grade-tonnage models. Initially, each of these three techniques are used to classify mineral deposits into 18 types. Analysis of these results suggested that some deposits types could be treated as a group and also that the classification could be divided into two levels: the first level to classify deposits considering groups of deposits and the second level to classify deposits previously identified on a group into some of specific type belonging to that group. A series of experiments was carried out in order to build a two levels model from the combination of the techniques used, which resulted in an average accuracy rate of 85% of cases. Patterns of errors occurrence were identified within groups in types of deposits less representative in the database. This represents a promising way to achieve improvement in the process of mineral deposits classification that does not mean increasing in the amount of deposits used or in the amount of characteristics of the deposits.