关键词: 属性约简, 特定类属性约简, 启发式约简算法, 邻域粗糙集, 邻域近似条件熵, 粒计算

Abstract:

Aiming at generalization of attribute reducts, the class-specific attribute reduct and its heuristic algorithm are established by neighborhood approximation condition-entropy. High classification-based neighborhood approximation condition-entropy is decomposed, and middle class-specific neighborhood approximation condition-entropy is extracted to achieve its double bounds and granulation non-monotonicity. Based on the new information measure, the class-specific attribute reduct is proposed, and a heuristic algorithm is accordingly designed. The relevant validity is finally verified by decision table examples and data set experiments. The obtained results are useful for uncertainty measurement and optimization application of class-specific pattern recognition.

Key words: attribute reduction, class-specific attribute reduction, heuristic reduction algorithm, neighborhood rough set, neighborhood approximation condition-entropy, granular computing