Tiago Agostinho Almeida

E-mail spam has become an increasingly important problem with a big economic impact in society. Fortunately, there are different approaches allowing to automatically detect and remove most of those messages, and the best-known techniques are based on Bayesian decision theory. However, such probabilistic approaches often suffer from a well-known difficulty: the high dimensionality of the feature space. Many term-selection methods have been proposed for avoiding the curse of dimensionality. Nevertheless, it is still unclear how the performance of Naive Bayes spam filters depends on the scheme applied for reducing the dimensionality of the feature space. In this paper, we study the performance of many term-selection techniques with several different models of Naive Bayes spam filters. Our experiments were diligently designed to ensure statistically sound results. Moreover, we perform an analysis concerning the measurements usually employed to evaluate the quality of spam filters. Finally, we also investigate the benefits of using the Matthews correlation coefficient as a measure of performance.

Neste trabalho é proposta uma meta-heurística baseada em técnicas da computação evolutiva que visa encontrar um conjunto de árvores geradoras mínimas, para problemas de grafos que possuem incertezas em relação às informações associadas aos parâmetros. Resolver problemas dessa natureza é um processo NP-Completo, pois envolve um número enorme de comparações. A fim de contornar essa complexidade, é proposto um sistema imunológico artificial capaz de explorar eficientemente o espaço de busca e de obter resultados satisfatórios, sem a necessidade de confrontar todas as soluções entre si.