Introduction Processing Results Task Description input data: about 2000 mammography reports from 3 Warsaw hospitals, output data: (for each report) very detailed information concerning:

This paper is concerned with the application of a treebased regression model to extract fuzzy rules from highdimensional data. We introduce a locally weighted scheme to the identification of Takagi-Sugeno type rules. It is proposed to apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs a...

With the success of model-driven development as well as component-based and service-oriented systems, models of software architecture are key artefacts in the development process. To adapt to changing requirements and improve internal software quality such models have to evolve while preserving aspects of their behaviour. To avoid the costly verification of refactoring steps on large systems we...

The rule extraction techniques have been widely developed and used for data mining in many application areas [1], such as medical diagnosis, decision-making, classification and prediction. Rough sets theory, proposed by Zdzislaw Pawlak in the early 1980’s [2], has been used as rule extraction method for machine learning, knowledge discovery, expert systems [3]. This technique has offered useful...

superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge ...

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...

We will in this paper identify some of the central problems of current techniques for rule extraction from recurrent neural networks (RNN-RE). Then we will raise the expectations of future RNN-RE techniques considerably and through this, hopefully guide the research towards a common goal. Some preliminary results based on work in line with these goals, will also be presented.

Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method. This method allows to give explanatio...

Neural network classifiers are known to be able to learn very accurate models. In the recent past, researchers have even been able to train neural networks with multiple hidden layers (deep neural networks) more effectively and efficiently. However, the major downside of neural networks is that it is not trivial to understand the way how they derive their classification decisions. To solve this...

Casting neural network weights in symbolic terms is crucial for interpreting and explaining the behavior of a network. Additionally, in some domains, a symbolic description may lead to more robust generalization. We present a principled approach to symbolic rule extraction based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressio...