Rough set-based empirical modeling with applications to control, pattern classification adn data mining

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The proposed research program is concerned with the fundamental research, algorithms and applications of rough set theory and its probabilistic extensions. The theory of rough sets and the related technologies deal in the most part with automated learning of classification algorithms from data. The rough set-based methods include techniques for the analysis of suitability of data for the learning purpose, techniques for the elimination of  useless information from data, while focusing on the most dominant classificatory factors, and   the techniques for automated formation of classification rules and decision tables from data.      The current proposal is focused on probabilistic approaches to rough sets, in particular on variable precision and probabilistic  rough set models. The probabilistic extensions allow to model stochastic relationships existing in data and to develop classifier systems operating with controlled  degree of uncertainty.      The application aspect of the proposal is focused on three major application domains. The first one is related to the theory, algorithms and experimental investigation of the problem of control algorithm acquisition from data through  machine learning. The key objective in this process is to substitute the complex mathematical modeling steps with automated generation of control algorithm from operation data.      The second application domain deals with the  methodology, algorithms and prototyping of selected pattern recognition applications. In particular, experiments will be conducted with  automated image classification such as medical image interpretation, face image recognition, text contents classification and  speaker-independent recognition of isolated spoken words.      The third broad application area is data mining in which the rough set-based algorithms will be used for analysis and modeling of probabilistic dependencies existing in data, detection of fundamental factors in the relationships and optimization of the derived models. The main focus will be on analysis and interpretation of medical data in cooperation with medical research institutions.