In the domain of knowledge discovery in databases and its computational part called data mining, many works addressed the problem of association rule extraction that aims at discovering relationships between sets of items (binary attributes). An example association rule fitting in the context of market basket data analysis is cereal ∧ milk → sugar (support 10%, confidence 60%). This rule states...

The rule extraction capability of neural networks is an issue of interest to many researchers. Even though neural networks o€er high accuracy in classi®cation and prediction, there are criticisms on the complicated and non-linear transformation performed in the hidden layers. It is dicult to explain the relationships between inputs and outputs and derive simple rules governing the relationship...

|This paper is concerned with knowledge extraction from reinforcement learners. It addresses two approaches towards knowledge extraction: the extraction of explicit , symbolic rules from neural reinforcement learners, and the extraction of complete plans from such learners. The advantages of such knowledge extraction include (1) the improvement of learning (especially with the rule extraction a...

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...

Rough sets theory is now becoming a mathematical foundation of soft comput ing. This theory makes use of equivalence relations defined for each set of attributes in any table, and applies the concept like definability of a set, dependency among attributes, reduction of data, rule extraction, etc., to data analysis. In this paper, a problem of knowledge discovering in the form of rules from any ...

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...

Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based info...

The problem facing the industry and the common user today is that of an information glut. Large amounts of useful information, in various forms, are being generated mostly for human consumption. This deluge of information requires us to find ways of gathering information from such unstructured sources with as little manual intervention as possible. This has been made possible by information ext...

This paper discusses learning in hybrid models that goes beyond simple classification rule extraction from backpropagation networks. Although simple rule extraction has received a lot of research attention, we need to further develop hybrid learning models that learn autonomously and acquire both symbolic and subsymbolic knowledge. It is also necessary to study autonomous learning of both subsy...