A Feature Selection Method Based on Hybrid Improved Binary Quantum Particle Swarm Optimization
As the volume of data available for analysis grows, feature selection is becoming a vital part of ensuring accurate classification results. In classification problems, selecting a small number of features reduces computational complexity, but selecting the right features is important to maintain a h...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8726344/ |