Abstract
When creating regression models from data the problem arises that the complexity of the models rapidly increases with the number of features involved. Especially in real world application where a large number of potential features are available, feature selection becomes a crucial task. In this paper we will present a novel approach to feature selection which uses bacterial optimization to identify the optimal set of features with respect to a given learning problem and a given learning algorithm. This approach ensures high accuracy and significantly increases interpretability of the resulting models.
| Original language | English |
|---|---|
| Title of host publication | Proc. Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems 2004 |
| Number of pages | 6 |
| Publication status | Published - 2004 |
Fields of science
- 101 Mathematics
- 101004 Biomathematics
- 101027 Dynamical systems
- 101013 Mathematical logic
- 101028 Mathematical modelling
- 101014 Numerical mathematics
- 101020 Technical mathematics
- 101024 Probability theory
- 102001 Artificial intelligence
- 102003 Image processing
- 102009 Computer simulation
- 102019 Machine learning
- 102023 Supercomputing
- 202027 Mechatronics
- 206001 Biomedical engineering
- 206003 Medical physics
- 102035 Data science