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| Author(s): |
Bruno Feres de Souza
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2010-10-26 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
Ricardo Bastos Cavalcante Prudêncio;
Ivan Nunes da Silva;
Carlos Manuel Milheiro de Oliveira Pinto Soares;
Fernando José von Zuben
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| Advisor: | André Carlos Ponce de Leon Ferreira de Carvalho; Carlos Manuel Milheiro de Oliveira Pinto Soares |
| Abstract | |
Among the most common applications involving microarray, one can highlight the classification of tissue samples, which is essential for the correct identification of the occurrence of cancer and its type. This classification takes place with the aid of machine learning algorithms. Choosing the best algorithm for a given problem is not trivial. In this thesis, we studied the use of meta-learning as a viable solution. The experimental results confirmed the success of the application using a standard framework for characterizing data and constructing the recommendation. Thereafter, some improvements were made in these two aspects. Initially, a new set of meta-attributes was proposed, which are based on cluster validation indices. Then the kNN method for ranking construction was extended to weight the influence of nearest neighbors. In the context of meta-regression, the use of SVMs was introduced to estimate the performance of ranking algorithms. Decision trees were also employed for recommending algorithms. Due to their low performance, a ensemble of trees was employed, which greatly improved the quality of results (AU) | |