基于免疫量子粒子群优化的测试代价敏感属性约简算法

A test-cost-sensitive attribute reduction algorithm based on immune quantum particle swarm optimization

为了解决测试代价敏感属性约简的高效性和准确性问题,提出一种基于免疫量子粒子群优化的最小测试代价属性约简算法。依据条件信息熵和测试代价因素定义适当的适应值函数,将最小测试代价属性约简问题转化为0-1组合优化问题,提出最小属性的属性约简问题是一种具有特殊测试代价的最小测试代价属性约简问题。最后结合量子粒子群和人工免疫方法给出约简算法。实验对比已有的最小属性约简算法和测试代价敏感属性约简算法,实验结果表明本算法是有效的。

In order to achieve high efficient and accurate test cost sensitive attribute reduction, we propose an algorithm for minimizing test cost reduction based on immune quantum particle swarm optimization. We define the proper fitness function according to conditional information entropy and test cost factors. The problem of the attribute reduction of the minimum test cost is converted to an optimi- zation problem of 0-1, and the problem of the minimum attribute reduction is equal to the attribute reduction problem of minimum test cost reduction with special test cost. Finally, the reduction algorithm is presented by combining the quantum particle swarm optimization and the artificial immune algorithm. We conduct experiments and compare the proposed algorithm with the existing minimum attribute reduction algorithm and test cost sensitive attribute reduction algorithm. Experimental results prove its effectiveness.