Abstract: To address the low reduction efficiency and limited classification performance of traditional dominance-based rough sets when the attribute scale expands, this paper introduces a generalized decision combination entropy derived generalized decision classes by integrating attribute dependency and information entropy. This measure reflects attribute importance from multiple perspectives. Based on this measure, attributes are ranked by importance, and a machine learning classifier is used to select an attribute subset with high classification accuracy. Comparative experiments validate the effectiveness of the proposed method.

Key words:  , dominance-based rough sets, generalized decision classes, generalized decision combination entropy, lower approximation operator