Since an tiquity, man has dreamed of building a de vice that would "learn from examples" 1 "form generalizations", and "discover t he rules" behind patt ern s in t he data. Recent work has shown that a high ly connected , layered networ k of simple an alog processing element s can be astonishingly successful at this, in some cases . In ord er to be precise about what has been observed, we give ...

Although backpropagation neural networks generally predict better than decision trees do for pattern classi cation problems they are of ten regarded as black boxes i e their predic tions are not as interpretable as those of deci sion trees This paper argues that this is be cause there has been no proper technique that enables us to do so With an algorithm that can extract rules by drawing paral...

A new constructive learning algorithm, called Hamming Clustering (HC), for binary neural networks is proposed. It is able to generate a set of rules in if-then form underlying an unknown classification problem starting from a training set of samples. The performance of HC has been evaluated through a variety of artificial and realworld benchmarks. In particular, its application in the diagnosis...

Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in th...