Learning | Partial Order Learning
The Partial Order Learning is a Meta-Learning algorithm that can be compared to Bagging. It is based on Data Perturbation (Smoothed Bootstrapping) for learning a set of networks that are transformed into Essential Graphs to define a set of Partial Orderings.
In a Partial Ordering, the index of a node is incremented only when it has parents in the Essential Graph. The indices of a Partial Ordering are saved as Temporal Indices.
There are 2 options for summarizing the node indices that have been estimated on the bag of networks:
- The mean value (a real value),
- A vote (an integer).
Once defined, the Temporal Indices are used for learning a network on the original unperturbed data set.