| Rough set theory , introduced by Pawlak in the early 1980s , is a new mathematical tool to deal with vagueness and uncertainty . Pawlak firstly created the conceptions: a quality of lower approximation and a quality of upper approximation, reduction attributes, core, boundary-area, etc. These conceptions essentially emphasized the reduction of the related information system. Meanwhile these conceptions acted as the base of the "Knowledge Discovery in Database"(KDD, or Data Mining). This approach seems to be of fundamental importance to artificial intelligence(AI) and cognitive sciences , especially in the areas of machine learning , knowledge acquisition , decision analysis , knowledge discovery from databases, expert systems , decision support systems , inductive reasoning, and pattern recognition.In particular, the rough set approach has found interesting applications set in medicine, pharmacology, business, banking, market research , engineering design , meteorology , vibration analysis , switching functions, conflict analysis , image processing , voice recognition , concurrent system analysis , decision analysis, character recognition, and other fields.This paper begins with the basic theory, then introduces the mainparts of the authors work. They are:(1)First part--research the Skowron discernibility matrix, thesignificant and relevance of attributes; Meanwhile, using the conditional entropy to propose a heuristic algorithm for attribute reduction; Then, correct the mistake in the reference [17]; finally, the experiment shows that it can get better effect and it also shows the reduction results of UCI database using this algorithm;(2)Second part-- It begins with the variable universal theory,then uses the discernibility matrix to define the undistributed class, which proposing a heuristic algorithm for attribute reduction; finally, the experiment shows that it can get better effect and it also shows the reduction results of UCI database using this algorithm. |
PDF Full Text Request