In this paper we present a strategy together with its computational implementation to intelligently analyze data clusters in terms of symbolic cluster-defining rules. We present a symbolic rule extraction workbench that leverages rough set theory to inductively extract CNF form symbolic rules from un-annotated continuous-valued data-vectors. Our workbench purports a hybrid rule extraction metho...

Various rule-extraction techniques using ANNs have been used so far, most of them being applied on multi-layer ANNs, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented, however, there are virtually no methods that view the extraction of rules from ANNs as systems which are independent from their archit...

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks. However, neural network ensembles, like networks, regarded black box because they cannot explain decision-making process. As result, despite high ensembles unsuitable some applications th...

This paper shows how knowledge, in the form of fuzzy rules, can be derizted from a superuised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by remooing excessiae recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usab...