Feature subset selection for multi-scale neighborhood decision information system via mutual information

• Published in: The Artificial intelligence review Vol. 57; no. 1; p. 15
• Main Authors: Zhang, Lujing, Lin, Guoping, Wei, Ling, Kou, Yi
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Data analysis; Datasets; Redundancy; Scale models; Neighborhood rough sets; Feature subset selection; Multi-scale; Mutual information
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-023-10626-w

As a granular computing model, multi-scale data analysis has attracted considerable attention in last several years. However, most of multi-scale models are hardly to deal with multi-source data, especially in heterogeneous environments. For this reason, we investigate a novel multi-scale model by combining weighted neighborhood rough sets with Wu-Leung model for the first time, and apply it in feature selection for multi-source heterogeneous multi-scale data. First, the multi-scale weighted neighborhood granules obtained and their properties are discussed. Second, the mutual information of multi-source heterogeneous multi-scale features is presented. Based on this, the definition of the redundancy of the features is obtained and a feature subset selection algorithm that simultaneously performs the selection of features and the optimal scale combination is given. Finally, numerical experiments on multi-source heterogeneous multi-scale datasets and heterogeneous multi-scale datasets are conducted to examine the effectiveness and feasibility of the proposed model. The experiments demonstrate that the proposed model can obtain better results on both datasets.