Constructing three-way classifier with interval granulation neighborhood rough sets based on uncertainty invariance

Three-way decision with neighborhood rough sets (3WDNRS) is effective in handling uncertain problems involving continuous data through the adjustment of the neighborhood radius. However, it faces two main limitations. Firstly, 3WDNRS relies on individual neighborhood granules as inputs, which can impair both decision efficiency and model generalizability. Secondly, the thresholds used in 3WDNRS often require predefinition based on prior knowledge, making the method difficult to apply in situations where such knowledge is lacking. To address these problems, this study introduces interval granulation (IG) into 3WD to construct an effective three-way classifier. Firstly, an interval granulation method based on DBSCAN is proposed. Then, an interval granulation neighborhood rough sets (IGNRS) model is presented, combining IG with quality indicators. Based on the IGNRS, a three-way classifier called 3WD-IGNRS is proposed by considering the principle of minimum fuzzy loss. Finally, extensive comparative experiments are conducted with three state-of-the-art granular-ball (GB)-based classifiers and four classical machine learning classifiers on 12 public benchmark datasets. The results demonstrate that our models consistently outperform the compared methods, achieving an average accuracy improvement of 4.94% compared to the best-performing granular-ball classifier.