查看文章

Various aspects of reduct approximations are discussed. In particular, we show how to use them to develop flexible tools for analysis of strongly inconsistent and/or noisy data tables. A special attention is paid to the notion of a rough membership decision reduct — a feature subset (almost) preserving the frequency based information about conditions-→decision dependencies. Approximate criteria of preserving such a kind of information under attribute reduction are considered. These criteria are specified by using distances between frequency distributions and information measures related to different ways of interpreting rough membership based knowledge.