System Uncertainty Measure Based on Entropy and Approximation Classification Quality in Rough Set

摘要：

Uncertainty is an intrinsic feature of decisionmaking information systems and thus plays an important role in their performance. The system uncertainty can be measured efficiently by rough group theory based methods, which has better performance and certain drawbacks when combined with entropy. Based on all known entropy and approximation classification quality, we proposed a new measure method which avoids the deficiency of all known entropy measures effectively. The measures was verified reasonable and effective by realizing the independent learning and improved the overall performance of the Skowron algorithm.

关键词： uncertainty measure rough sets all known entropy approximation classification quality self-learning

作者: ZHANG Yan-lin

作者单位: College of Computer Science and Technology Chongqing University of Posts and Telecommunications chongqing,China

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 542-545

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）