Knowledge Reduction Based on Rough Sets and Immune Network Algorithm

摘要：

We propose a new reduction strategy based on rough sets and Immune Network Algorithm (INA).Rough sets have been used as knowledge reduction method with much success, but current hillclimbing rough set approaches to knowledge reduction are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized data sets. So, stochastic approaches provide a promising feature selection mechanism. By introducing the immune system,the immune network algorithm is proposed.Then the minimal relative reduction is computing by the immune network algorithm.The results show that INA is efficient for rough set-based knowledge reduction .

作者: Changcheng Xiang Daijun Wei Xiyue Huang

作者单位: Automation College ,Department of math Chongqing University,Hubei Institute of Nationalities Chongqi Automation College Chongqing University Chongqing, P.R.China 400030

会议类型: 国际会议

会议名称: Fourth International Conference on Impulsive and Hybrid Dynamical Systems(ICIHDS 2007)(第四届国际脉冲和混合动力系统学术会议)

会议地点: 南宁

会议语种:英文

在线出版日期: 2007-07-20（万方平台首次上网日期，不代表论文的发表时间）