He has PhD degree in Computer Science and Computational Mathematics at the University of São Paulo (USP), in the research line Computational Intelligence. He holds a degree in Computer Science from the Federal University of Rio Grande do Norte (2002) and a Master's Degree in Applied Informatics from the University of Fortaleza (2009). During the doctorate, he had conducted two stages of research abroad. Being the first at the University of Birmingham (England) under the supervision of Prof. Xin Yao and the second at the University of Leiden (Netherlands) under the supervision of Prof. Joost Kok. He has an academic background in Computer Science, with an emphasis on Artificial Intelligence working mainly on the following topics: clustering, pattern recognition, data mining, ensembles of classifiers and evolutionary algorithms. Outside the academic area, he has experience in Software Testing as a result of his professional performance at Bank of the Northeast of Brazil S.A. for ten years. (Source: Lattes Curriculum)
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In credit analysis applications, the databases are often heavily unbalanced, presenting a much larger number of examples from the negative class (complaint clients) than from the positive class (default clients). Unbalanced Databases compromise the performance of most classical classification algorithms because they assume a balanced distribution of examples between the classes and consi…
In many practical classification problems, the data set used for the induction of a classifier is significantly unbalanced. This occurs when the number of instances of a particular class is much lower than the number of instances in the other(s) class(es). Unbalanced data sets can compromise the performance of most classical classification algorithms, since these algorithms assume a balan…
In many practical classification problems, the data set used for the induction of a classifier is significantly unbalanced. This occurs when the number of instances of a particular class is much lower than the number of instances in the other(s) class(es). Unbalanced data sets can compromise the performance of most classical classification algorithms, since these algorithms assume a balan…
| 2 / 2 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 4 / 4 | All Scholarships |
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