"Combinação de classificadores simbólicos para melhorar o poder preditivo e descritivo de Ensembles"

Abstract

T he hypothesis quality induced by current machine learning algorithms depends mainly on the quantity and quality of features and examples used in the training phase. Frequently, hypothesis with low precision are obtained in experiments using large databases with a large number of irrelevant features. Thus, one active research area in machine learning is to investigate techniques able to extend the capacity of machine learning algorithms to process a large number of examples, features and classes.To learn concepts from large databases using machine learning algorithms, two approaches can be used. The first approach is based on a selection of relevant features and examples, and the second one is the ensemble approach. An ensemble is a set of classifiers whose individual decisions are combined in some way to classify a new case. Although ensembles classify new examples better than each individual classifier, they behave like black-boxes, since they do not offer any explanation to the user about their classification.The purpose of this work is to consider a form of symbolic classifiers combination to work with large databases. Given a large database, it is equally divided randomly in small databases. These small databases are supplied to one or more symbolic machine learning algorithms. After that, the rules from the resulting classifiers are combined into one classifier. To analise the viability of this proposal, was implemented a system in logic programming language Prolog, called R ule System. This system has two purposes; the first one, implemented by the Rule Analises Module, is to evaluate rules induced by symbolic machine learning algorithms; the second one, implemented by the Combination and Explanation Module, is to evaluate several forms of combining symbolic classifiers as well as to explain ensembled classification of new examples. Both principal modules constitute the R ule System. This work describes ensemble construction methods and combination of classifiers methods found in the literature; the project and documentation of R ule System; the methodology developed to document the R ule System; and the implementation of the Combination and Explanation Module. Two different case studies using the Combination and Explanation Module are described. The first case study uses an artificial database. Through the use of this artificial database, it was possible to improve several of the heuristics used by the the Combination and Explanation Module. A real database was used in the second case study.

"Pós-processamento de regras de associação"

“…Além dos trabalhos já implementados descritos aqui, cabe ressaltar que diversos outros trabalhos desenvolvidos ou em desenvolvimento pelos pesquisadores do LABIC com funcionalidades para processamento de dados e textos, Aprendizado de Máquina e processamento de padrões serão integrados ao projeto Discover, entre os quais podem ser citados: Horst (1999a), Nagai (2000), Lee (2000), Imamura (2001), Bernardini (2002), Gonçalves (2002), Geromini (2002), Prati (2003), Bernardini (2003), Martins (2003), Lee (2003), Pugliesi (2004), Matsubara (2004), Ferro (2004).…”

"Aplicação de técnicas de data mining em logs de servidores web"

“…Nosúltimos anos, diversos pesquisadores de nosso laboratório de pesquisa (LABIC 2 ) têm utilizado algoritmos de Aprendizado de Máquina em suas pesquisas, desenvolvendo, na forma de scripts Perl, uma série de ferramentas para facilitar a configuração e execução de experimentos (Prati, Baranauskas, and Monard 2001b), (Prati, Baranauskas, and Monard 2001a), (Batista 2001), (Kemp, Batista, and Monard 2001), (Baranauskas 2001), , (Bernardini 2002), (Gomes 2002), (Imamura 2001), (Milaré 2000), (Paula 2003), (Pila 2001), (Pugliesi 2001).…”

Investigação de regressão no processo de mineração de dados

“…Apesar dos trabalhos já implementados descritos aqui, a grande maioria das ferramentas e sistemas que vão compor o DISCOVER estão sendo desenvolvidos pelos pesquisadores do nosso laboratório, com funcionalidades para processamento de dados (Batista, 2003) e textos (Imamura, 2001;Martins, 2001; Gonçalves, 2002), aprendizado de máquina (Pila, 2001;Bernardini, 2002;Gomes, 2002) e processamento de padrões (Lee, 2000;Baranauskas, 2001;Pugliesi, 2001;Melanda, 2002;Paula, 2003;Milaré, 2003). (Prati, Baranauskas, & Monard, 2001b) e (Baranauskas, 2001).…”