These components reduce the set of actives examples before an analysis. Examples is subdivided in active and inactive examples.
If the learning process is realized on active examples, projection (new attribute) is always applied to all the examples.
Instance selection components
Sequential sampling, see Vitter's work.
|- Size of sample: proportion or number of examples.|
- Stratification attribute, a discrete one.
- Stratification type : balanced or proportional.
- Size of the sample: proportion or number of examples.
|A discrete attribute must be available in the dataset.|
|Permutation of the status of examples: actives become inactive, and conversely.||- Set to be active: inactive or all examples.|