Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

In this paper we propose a generalization to symbolic interval valued variables of the Principal Curves and Surfaces method proposed by T. Hastie in [4]. Given a data set X with n observations and m continuos variables the main idea of Principal Curves and Surfaces method is to generalize the principal component line, providing a smooth one-dimensional curved approximation to a set of data poin...

Numerical methods based on the conceptual framework of symbolic analysis are important tools for the study of non-linear dynamical systems. Implementations of these concepts have been applied successfully to many investigation tasks, like the localization of the chain recurrent set, attractors, and their domains of attraction as well as the computation of the Morse spectrum and verification of ...

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed provide insights into the GNN decision making process. These are first important steps on way explainability, but generated explanations often hard understand for users that not AI experts. To overcome this problem, we introduc...

Dimension reduction is one of the important requirements for a successful representation to improve the efficiency of extracting the attracting trend patterns on the time series. Furthermore, an efficient and accurate similarity searching on a huge time series data set is a crucial problem in data mining preprocessing. Symbolic representations have proven to be a very effective way to reduce th...

Intuitively, one can assume that imitating a movement is an easier task than responding to a symbolic stimulus like a verbal instruction. Support for this suggestion can be found in neuropsychological research as well as in research on stimulus-response compatibility. However controlled experimental evidence for this assumption is still lacking. We used a stimulus-response compatibility paradig...