Abstract
The “curse of dimensionality” is a bottleneck in big data and artificial intelligence. To reduce the dimensionality of data using the minimal vertex covers of graphs, a discernibility matrix can be applied to construct a hypergraph. However, constructing a hypergraph using a discernibility matrix is a time-consuming and memory-consuming task. To solve this problem, we propose a more efficient approach to graph construction based on a description vector. We develop a graph-based heuristic algorithm for feature selection, named the graph-based description vector (GDV) algorithm, which is designed for fast search and has lower time and space complexities than four existing representative algorithms. Numerical experiments have shown that, compared with these four algorithms, the average running time of the GDV algorithm is reduced by a factor of 36.81 to 271.54, while the classification accuracy is maintained at the same level.
Original language | English |
---|---|
Pages (from-to) | 746-759 |
Number of pages | 14 |
Journal | Information Sciences |
Volume | 629 |
DOIs | |
Publication status | Published - Jun 2023 |
Externally published | Yes |
Keywords
- Description vector
- Feature selection
- Hypergraph
- Rough sets
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence