计算机工程 ›› 2009, Vol. 35 ›› Issue (12): 148-150.doi: 10.3969/j.issn.1000-3428.2009.12.052

• 人工智能及识别技术 • 上一篇    下一篇

基于量子粒子群优化的最小属性约简算法

王加阳,谢 颖   

  1. (中南大学信息科学与工程学院,长沙 410083)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-06-20 发布日期:2009-06-20

Minimal Attribute Reduction Algorithm Based on Quantum Particle Swarm Optimization

WANG Jia-yang, XIE Ying   

  1. (School of Information Science and Engineering, Central South University, Changsha 410083)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-06-20 Published:2009-06-20

摘要: 属性约简是粗糙集理论中的一个核心问题,为了有效获取属性最小相对约简,提出一种基于量子粒子群优化算法的粗糙集属性约简算法。该算法通过引入自适应参数使得算法在保证取得的是一个约简的情况下尽可能地减少所包含的属性数目,并期望能够获得理想的约简结果。试验结果证明该算法能有效地进行属性约简,并取得良好的约简结果。

关键词: 属性约简, 粒子群优化, 群体智能, 量子, 粗糙集

Abstract: Attribute reduction is a key point of rough set theory. In order to get minimal subsets of attributes, this paper proposes a rough set attributes reduction algorithm based on Quantum Particle Swarm Optimization(QPSO). A parameter selection method is involved into the algorithm. It not only keeps the ability in getting reduction but also deduces the number of attribution, and it can obtain the prime effect. Experimental result proves the efficiency of the algorithm.

Key words: attribute reduction, Particle Swarm Optimization(PSO), swarm intelligence, quantum, rough set

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