基于粒子群的不完备决策表属性约简PSOIDTAR法

属性约简是粗糙集理论的一个核心部分。由于经典的粗糙集模型对不完备信息系统不适应，通过把属性约简问题归结为0-1组合优化问题，提出了一种应用二进制粒子群算法来求解属性约简的方法。通过引入近似分类精度和近似分类质量，为获得最小约简确定了有效合理的粒子适应度函数。仿真实验结果表明该算法能得到最小相对约简，且具有较高的运算效率。

PSOIDTAR method based on particle swarm optimization for attribution under incomplete decision-making table

Attribution reduction is the core of the rough set theory.Because the theory of the classical rough set can't apply to the incomplete decision-making table,the paper proposes a rough set attribute reduction algorithm based on the binary Particle Swarm Optimization(PSO),taking the attribution reduction to a part of the 0-1 combination optimization problem.The paper imports approximate classified precision and the approximate classified quality,and makes an efficient particle adaptive function in order to get a best reduction.Finally,experimental results show the algorithm can find the minimal relative reduction,and it is fast and effective.