Détecteurs de fuite

bilita o uso de ensembles compostos por árvores de decisão em aplicações de credit-scoring ;

5.2

Trabalhos futuros

Pela possibilidade de variação dos métodos adotados em cada uma das fases da arquitetura proposta, é possível configurá-la de uma forma muito diversificada. Por isso, alguns trabalhos que poderiam ser desenvolvidos a partir de agora seriam:

• Estudar o uso da arquitetura proposta, adotando na fase de poda o mé- todo Ensemble Pruning via Individual Contribution Ordering (EPIC) (Lu

et al., 2010). O EPIC utiliza como critérios de seleção dos classificadores

que formarão o subensemble algumas de suas características individuais, além de medidas de diversidade entre eles. O EPIC superou em desempe- nho o método Orientation-Ordering em experimentos realizados sobre 26 bases de dados UCI, considerando problemas de aprendizagem en- volvendo classificação binária. Embora, dentre estas bases, não estejam relacionadas as utilizadas nos experimentos deste estudo – Australian e

German, é possível que, ao se aplicar este método na arquitetura proposta

neste trabalho, a mesma apresente ainda um melhor desempenho do que com o OO;

• Investigar o desempenho da arquitetura na solução de outros tipos de aplicações, inclusive aquelas que envolvam problemas com múltiplas clas- ses, adotando outros modelos de classificadores base, como por exemplo: redes neurais artificiais ou máquinas de vetor de suporte. É possível que, neste caso, seja interessante ajustar o método de combinação na fase de teste da arquitetura, saindo de uma abordagem no nível abstrato, como o Majority Vote, para outros métodos que possuam abordagens de rank ou medição;

• Avaliar, como alternativa para tratar a questão de redundância de atri- butos, a possibilidade de combinação de outros métodos de seleção de características, aleatórios ou não, ou ainda métodos de extração de carac- terísticas durante a fase de treinamento.

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