Title :
Approximate Reduction Algorithm Based on Rough Set Theory
Author :
Bin, Shao ; Yunliang, Jiang ; Qing, Shen
Author_Institution :
Sch. of Inf. Eng., Huzhou Teachers Coll., Huzhou
Abstract :
The attribute reduction of information system can enhance accuracy and efficiency of knowledge discovery, machine learning, etc. After studying reduction strategy in rough set theory, the concept of approximate reduction and an approximate reduction algorithm are proposed in this paper. This algorithm can retain minimal attributes in the basic style of information system, i.e. reduce as many attributes as possible. That can save much time for the system´s later disposal. The algorithm´s time complexity hasnpsilat been improved, but attributes after reduction are reduced greatly. The original information system has a certain loss, but this can be accepted under a certain significance level. Lastly, the reduction strategy is compared with approximate reduction strategy by nine attributes which belong to the information system.
Keywords :
artificial intelligence; information systems; rough set theory; approximate reduction algorithm; discernibility matrix; machine learning; rough set theory; Educational institutions; Electronic mail; Error probability; Heuristic algorithms; Information systems; Knowledge engineering; Machine learning; Machine learning algorithms; Predictive models; Set theory; Approximate reduction; Attribute reduction strategy; Discernibility matrix; Rough set;
Conference_Titel :
Cyberworlds, 2008 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-0-7695-3381-0