Interval-set decision information tables extend classical decision information tables. However, few studies have focused on its heuristic reduction. To solve the problem in interval-set decision information tables, this paper proposed attribute reduction and its heuristic reduction algorithm by adopting the model positive region and corresponding dependency degree. In the interval-set rough set model, it defined the positive region and dependency degree for decision classification, and proved the granulation monotonicity. It established attribute reduction based on the dependency degree and designed a heuristic reduction algorithm. The instance analysis and the experimental results show that the proposed attribute reduction algorithm is effective. The obtained results are useful for dependency learning and feature optimization.

[2021-11-05] Printed Article /doc/2021.04.0135/v2