Definition
Instance selection aims to improve computational efficiency and model performance by removing redundant or noisy data points. Unlike feature selection, it operates on the rows of the dataset. The goal is to find a smaller subset that preserves the essential information needed for learning, thereby speeding up training times and potentially reducing overfitting.
Summary
A preprocessing technique that reduces the size of a dataset by selecting a subset of representative instances.
Key Concepts
- Data reduction
- Noise removal
- Representative subset
- Computational efficiency
Use Cases
- Large-scale dataset preprocessing
- Speeding up nearest neighbor searches
- Cleaning imbalanced datasets