Extração de conhecimento simbólico de redes neurais.
Resumo
The fact that Artificial Neural Networks (ANNs) are not able to explain, in a symbolic
way, neither their decisions or the knowledge embedded in its connections and architecture
is a well-known limitation. This work investigates several methods of knowledge extraction
from ANNs proposed in the literature. More specifically, it focuses on four different
approaches for knowledge extraction that are detailed and criticized and, for each of them,
discusses a possible implementation. Also, a taxonomy for methods of rule extraction from
ANNs, found in the literature, is detailed. An extension of this taxonomy aiming at a more
useful, refined and versatile version is proposed. The main goal of the work, however, is to
approach knowledge extraction from ANN in a critical way, analyzing each of the four
methods concerning, mainly, their scopes, limitations and effective contribution to
improving readability and easy understanding.