Modeling Processes of Inferring Good Maximally Redundant Tests

نویسندگان

  • Xenia Naidenova
  • Vladimir Parkhomenko
چکیده

Good test analysis is considered. Two kinds of classification subtasks are defined: attributive and object ones. Some ideas of modeling and optimization of inferring good maximally redundant tests are formalized. An algorithm of inferring good maximally redundant tests based on the decomposition into attributive subtasks is given, where good maximally redundant tests are regarded as concepts of formal concept analysis. An approach to incremental inferring good maximally redundant tests is considered. Keywords—Good classification test, modeling, Galois mappings, decomposition, implications, inferring rules, incremental, formal concept analysis.

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تاریخ انتشار 2015