Research

Our research focuses on natural inspired methods in machine learning. Neural networks, evolutionary algorithms, neuroevolution, etc. We apply our algorithms in data mining and artificial intelligence. We study ensembling approaches and meta-learning in predictive modelling, recommender systems or data clustering.

Disadvantage of black-box modelling results from its multidimensionality and complexity. Where the traditional modelling methods fail due to the “curse of dimensionality” phenomenon, inductive methods are capable to build reliable models.

To overcome the black-box disadvantage we recommend using the visualisation of models' behaviour. We are developing several visualization techniques that can be directly used for the visual knowledge mining. Pictures showing models' behaviour are very useful especially for complex systems, where math equations are not interpretable any more.

Our research is concentrated also in the field of knowledge extraction. It means that useful information hidden inside a black-box model is found and represented in a comprehensible manner (e.g. in form of simple math formula, or a figure).

For time context based data the recurrent type of artificial neural network can be used. Combined with the appropriate visualisation technique or with method of the finite state machine description from the recurrent neural network behaviour extraction, it could give us the new view to the data structure in time (knowledge mining, see above). Combination of this recurrent networks theory and its behaviour visualisation methods with fuzzy logic will be the part of the next research.

Many signals produced in the real environment have temporal meaning. Such signal should be modelled using systems suitable for processing of signals instead of system for static and steady data. One can perform tasks such as recognition of signals and their parts, prediction of time series, control and reasoning based on a generated model. We use statistical models such as Hidden Markov Models and recurrent neural networks such as extensions of Categorizing and Learning Module. Various visualization techniques help us to understand the model and a sense of the modelled signal and help a user to exploit them directly in future industrial applications.

Hardware support for fast simulation of adaptive system like neural networks is the next objective of our research. Objectives are to find methods for optimization of simulation models with respect to fast hardware implementation. Various approximations simplificating models but preserving their major behaviour are used.