Convex granules and convex covering rough sets

• Published in: Engineering applications of artificial intelligence Vol. 124; p. 106509
• Main Authors: Long, Zhuo, Cai, Mingjie, Li, Qingguo, Li, Yizhu, Cai, Wanting
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Convex hull; CrossSift; Information granule; Rough sets; Information granule; Convex hull; Rough sets; CrossSift
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.106509

Many extensions of rough sets have been trying to seek appropriate granular structures, such as neighborhood systems, disjoint intervals and coverings. However, few of them consider data-driven approaches to generating posets-structured coverings based on granules of irregular shapes and variable sizes. By generalizing norm granules (intervals, δ-neighborhoods and k-nearest neighbors), the present study proposes a tree-structured model whose information granules are obtained through an “onion-peeling” strategy, CrossSift. Two comparative experiments are conducted in this paper. One shows that granules generated by CrossSift are able to achieve a higher dependency degree with fewer numbers than equal width/frequency intervals, δ-neighborhoods and k-nearest neighbors. The other shows the trees output by CrossSift outperform SVC, KNN, AdaBoost, Cart, LDA in the average rank of classification accuracy. The proposed method bridges a gap between rough sets and perceptrons, and is expected to contribute to dimensionality reduction, computer vision and geometry.