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We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning(More)
We consider a synthesis of complex objects by multi-agent system based on rough mereological approach. Any agent can produce complex objects from parts obtained from his sub-agents using some composition rules. Agents are equipped with decision tables describing (partial) speciications of their synthesis tasks. We investigate some problems of searching for(More)
Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed(More)
We present an efficient method for decision tree construction from large data sets, which are assumed to be stored in database servers, and be accessible by SQL queries. The proposed method minimizes the number of simple queries necessary to search for the best splits (cut points) by employing " divide and conquer " search strategy. To make it possible, we(More)