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 optimization 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.

 

attribute reduction, test-cost-sensitive, particle swarm optimization, fitness function, minimum attribute reduction

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