A fast approach to attribute reduction from perspective of attribute measures in incomplete decision systems

Attribute measures, used to evaluate the quality of candidate attributes, play an important role in the process of attribute reduction. They largely affect the computational efficiency of attribute reduction. Existing attribute measures are acted on the whole universe in complete decision systems. There are few studies on improving attribute reduction algorithms from the perspective of attribute measures in incomplete decision systems, which motivates the study in this paper. This paper proposes new attribute measures that act on a dwindling universe to quicken the attribute reduction process. In particular, the monotonicity guarantees the rationality of the proposed attribute measures to evaluate the significance of candidate attributes. On this basis, the corresponding attribute reduction algorithms are developed in incomplete decision systems based on indiscernibility relation and discernibility relation, respectively. Finally, a series of comparative experiments are conducted with different UCI data sets to evaluate the performance of our proposed algorithms. The experimental results indicate that the proposed algorithms are efficient and feasible.