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In data mining application, the test-cost-sensitive attribute reduction is an important task which aims to decrease the test cost of data. In operational research, the set cover problem is a typical optimization problem and has a long investigation history compared to the attribute reduction problem. In this paper, we employ the methods of set cover problem to deal with the test-cost-sensitive attribute reduc- tion. First, we equivalently transform the test-cost-sensitive reduction problem into the set cover problem by using a con- structive approach. It is shown that computing a reduct of a decision system with minimal test cost is equal to computing an optimal solution of the set cover problem. Then, a set- cover-based heuristic algorithm is introduced to solve the
test-cost-sensitive reduction problem. In the end, we con- duct several numerical experiments on data sets from UCI machine learning repository. Experimental results indicate that the set-cover-based algorithm has superior performances in most cases, and the algorithm is efficient on data sets with
many attributes.