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 …

Whereas newer machine learning techniques, like artifficial neural net-works and support
vector machines, have shown superior performance in various benchmarking studies, the …

This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression
problems. Although EAs have proven their ability to explore large search spaces, they are …

This paper proposes a new framework and method for extracting regression rules from
neural networks trained with multivariate data containing both nominal and numeric …

In this study, a new estimation method for the kinetic parameters in a distributed activation
energy model (DAEM) was designed and developed. In the proposed method, the …

We propose a new unsupervised training method for acquiring probability models that
accurately segment Chinese character sequences into words. By constructing a core lexicon …

Active research into processes and techniques for extracting the knowledge embedded
within trained artificial neural networks has continued unabated for almost ten years. Given …

We propose a multilogistic regression model based on the combination of linear and product-
unit models, where the product-unit nonlinear functions are constructed with the product of …

This paper describes RULEX, a technique for providing an explanation component for local
cluster (LC) neural networks. RULEX extracts symbolic rules from the weights of a trained …