Knowledge Extraction: A Comparison between Symbolic and Connectionist Methods


The use of a linguistic representation for expressing knowledge acquired by learning systems is an important issue as regards to user understanding. Under this assumption, and to make sure that these systems will be welcome and used, several techniques have been developed by the artificial intelligence community, under both the symbolic and the connectionist approaches. This work discusses and investigates three knowledge extraction techniques based on these approaches. The first two techniques, the C4.5 and CN2 symbolic learning algorithms, extract knowledge directly from the data set. The last technique, the TREPAN algorithm extracts knowledge from a previously trained neural network. The CN2 algorithm induces if...then rules from a given data set. The C4.5 algorithm extracts decision trees, although it can also extract ordered rules, from the data set. Decision trees are also the knowledge representation used by the TREPAN algorithm.

DOI: 10.1142/S0129065799000265

Cite this paper

@article{Nobre1999KnowledgeEA, title={Knowledge Extraction: A Comparison between Symbolic and Connectionist Methods}, author={C. Nobre and E. Martineli and Ant{\^o}nio de P{\'a}dua Braga and Andr{\'e} Carlos Ponce de Leon Ferreira de Carvalho and S. Rezende and Jos{\'e} L. Braga and Teresa Bernarda Ludermir}, journal={International journal of neural systems}, year={1999}, volume={9 3}, pages={257-64} }