In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by
promoting two complementary activities—symbolic knowledge extraction (SKE) and …

To date, the preponderance of techniques for eliciting the knowledge embedded in trained
artificial neural networks (ANN's) has focused primarily on extracting rule-based …

Symbolic knowledge-extraction (SKE) techniques are becoming of key importance for AI
applications since they enable the explanation of opaque black-box predictors, enhancing …

Abstract Machine learning opaque models, currently exploited to carry out a wide variety of
supervised and unsupervised learning tasks, are able to achieve impressive predictive …

This paper deals with an evaluation and comparison of the accuracy and complexity of
symbolic rules extracted from radial basis function networks and multilayer perceptrons …

Artificial neural networks (ANNs) are mathematical models inspired from the biological
nervous system. They have the ability of predicting, learning from experiences and …

Adaptive control technologies that incorporate learning algorithms have been proposed to
enable automatic flight control and vehicle recovery, autonomous flight, and to maintain …

Radial basis neural (RBF) networks provide an excellent solution to many pattern
recognition and classification problems. However, RBF networks are also a local …

This paper describes an algorithm to extract rules from a dynamic cell structure (DCS) neural
network and the rationale for extracting these rules. The DCS is a form of self-organizing …

Today, decision support systems based on predictive modeling are becoming more
common, since organizations often collect more data than decision makers can handle …