Discovery of Decision Rules fromExperimental
نویسندگان
چکیده
The problem of decision rules extracting (or more general knowledge discovery) from experimental data is intensively studied (see e. 27]). We apply rough set methods and boolean reasoning for decision rules discovery from decisions tables. It is not possible in general to extract general laws from experimental data by computing rst all reducts of a decision table representing data and next decision rules from these reducts. We investigate a problem how information about changes of reducts in random samples of the decision table can be used to generate these laws. The reducts stable in the process determined by diierent samples of decision table are called dynamic reducts. The set of decision rules is generated from theses dynamic reducts 2], 3]. We present also applications of a new idea of dynamic decision rules for object classiication. We report the results of experiments with monk's problem data, market data and handwritten digits. The results are showing that dynamic reducts and dynamic rules can help to extract laws from decision tables.
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