Approximate Reduction Algorithm Based on Rough Set Theory

Bin Shao, Yunliang Jiang, Qing Shen

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.