Grafting (decision trees)

Grafting is the process of adding nodes to inferred decision trees to improve the predictive accuracy.

Then they are grafted to the existing tree to improve the decision making process.

Grafting can identify regions where there are no occupancy and correct the poor class assignments which increases the accuracy.

The extension to graft multiple branches at each leaf reduces the number of errors.

However, the potential new branches have to be selected carefully to avoid increasing the error and failure cases.