Modified Great Deluge for Attribute Reduction in Rough Set Theory
Attribute reduction can be defined as a process of selecting a minimal subset of attributes (based on a rough set theory as a mathematical tool) from an original set with least lose of information. In this work, a modified great deluge algorithm has been employed on attribute reduction problems, where the search space is divided into three regions. In each region, the water level is updated using a different scheme based on the quality of the current solution, instead of using a linear mechanism which is used in the original great deluge algorithm. The proposed approach is tested on 13 standard benchmark datasets and able to obtain promising results when compared to state-of-the-art approaches.
Rough Set Theory Attribute Reduction Great Deluge Algorithm.
Majdi Mafarja Salwani Abdullah
Data Mining and Optimisation Research Group (DMO) Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
国际会议
上海
英文
1517-1522
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）