Knowledge Extraction from Local Function Networks

Knowledge Extraction from the Neural ‘Black Box’ in Ecological Monitoring

Phytoplankton biomass within the Saginaw Bay ecosystem (Lake Huron, Michigan, USA) was characterized as a function of select physical/chemical indicators. The complexity and variability of ecological systems typically make it difficult to model the influences of anthropogenic stressors and/or natural disturbances. Here, Artificial Neural Networks (ANNs) were developed to model chlorophyll a con...

Rule-Extraction from Radial Basis Function Networks

Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classi cation problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classication task and a vibration diagnosis classi cation task...

NEURAL NETWORKS AND GENETIC ALGORITHMS NEURAL NETWORKS AND GENETIC ALGORITHMS NEURAL NETWORKS Knowledge Extraction from Local Function Networks

Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be “shared” across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by ex...

The Extraction and Comparison of Knowledge from Local Function Networks

An Automated Rule Refinement System

Artificial neural networks (ANNs) are essentially a ‘black box’ technology. The lack of an explanation component prevents the full and complete exploitation of this form of machine learning. This chapter presents an historical perspective on rule extraction from artificial neural networks beginning with 6 ways in which the ANN paradigm may be enriched through the addition of rule extraction / e...