关键词: 决策粗糙集（DTRS）, 代价敏感, 属性约简

Abstract: This paper studies the test-cost sensitive reduction on the positive region of decision theoretic rough sets (DTRS). Based on the traditional positive region-based reduction, this paper takes test cost into account for the purpose of discovering the positive region-based reduction with the minimum total test cost, and adopts the simulated annealing algorithm combined with traditional positive region-based reduction of DTRS algorithm to search for the result of the positive region-based reduction with the minimum test cost. Furthermore, this paper proposes a test-cost sensitive positive region-based reduction algorithm for DTRS (TCSPR), and analyzes the time complexity of the algorithm. The experimental results confirm the effectiveness of TCSPR that can find a positive region reduction with fewer attributes and less test cost in polynomial time, and whose solution is generally the optimum solution or second-best solution of the optimization objective, viz. the positive region-based reduction with the minimum total test cost, and whose classification ability is hardly reduced in part of the data sets.

Key words: decision theoretic rough sets (DTRS), cost sensitive, attribute reduction