Data mining with a parallel rule induction system based on gene expression programming

Abstract

A parallel rule induction system based on gene expression programming (GEP) is reported in this paper. The system was developed for data classification. The parallel processing environment was implemented on a cluster using a message-passing interface. A master-slave GEP was implemented according to the Michigan approach for representing a solution for a classification problem. A multiple master-slave system (islands) was implemented in order to observe the co-evolution effect. Experiments were done with ten datasets, and algorithms were systematically compared with C4.5. Results were analysed from the point of view of a multi-objective problem, taking into account both predictive accuracy and comprehensibility of induced rules. Overall results indicate that the proposed system achieves better predictive accuracy with shorter rules, when compared with C4.5.

Métodos de Aprendizado de Máquina Aplicados ao Reconhecimento Autoral e Temporal de Bandas de Rock and Roll

“…Todos os métodos de classificação aplicados no Weka foram validados a partir de validação cruzada com dez folds, ou seja, o dataset foi automaticamente dividido em dez partes de igual tamanho onde nove partes foram usadas para treinamento e uma parte para validar o método encontrado. Isto é repetido 10 vezes comutando-se as partes [11].…”