Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem
In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have so...
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| Diğer Yazarlar: | |
| Materyal Türü: | Dissertação |
| Dil: | por |
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Universidade Federal do Rio Grande do Norte
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| Online Erişim: | https://repositorio.ufrn.br/jspui/handle/123456789/18045 |
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| Özet: | In the world we are constantly performing everyday actions. Two of these actions are
frequent and of great importance: classify (sort by classes) and take decision. When we
encounter problems with a relatively high degree of complexity, we tend to seek other opinions,
usually from people who have some knowledge or even to the extent possible, are
experts in the problem domain in question in order to help us in the decision-making process.
Both the classification process as the process of decision making, we are guided by
consideration of the characteristics involved in the specific problem. The characterization
of a set of objects is part of the decision making process in general. In Machine Learning
this classification happens through a learning algorithm and the characterization is applied
to databases. The classification algorithms can be employed individually or by machine
committees. The choice of the best methods to be used in the construction of a committee
is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting
the best configuration parameters of homogeneous committees for applications in
various classification problems. These parameters are: the base classifier, the architecture
and the size of this architecture. We investigated nine types of inductors candidates for
based classifier, two methods of generation of architecture and nine medium-sized groups
for architecture. Dimensionality reduction techniques have been applied to metabases looking
for improvement. Five classifiers methods are investigated as meta-learners in the
process of choosing the best parameters of a homogeneous committee. |
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