Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism. More...
< RETURNTYPE, DIMTYPE, SUBSET,
Public Member Functions
|void||set_gamma (const RETURNTYPE gamma=1.0)|
|void||set_delta (const unsigned int delta=1)|
|virtual std::ostream &||print (std::ostream &os) const|
Protected Member Functions
|virtual void||initialize (const DIMTYPE d, const DIMTYPE n, const PCriterion crit)|
|called before search - enables set-up of additional structures in descendants |
|virtual void||process_leafs ()|
|can be overridden to implement prediction information learning, threading etc. |
|virtual void||pre_evaluate_availables ()|
|assign values to each feature in availables - to be used for node ordering |
|virtual void||post_process_tree_level ()|
|enables to substitute missing COMPUTED values in nodes just after level creation, if needed |
|virtual bool||cut_possible ()|
|tests current node for the possibility to cut its sub-branch |
Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism.
FBB is in most feature selection tasks the fastest of all Branch & Bound algorithms and as such should be the method of first choice whenever optimal feature selection is required and possible (see the warning below). Nevertheless, the FBB's prediction mechanism can theoretically fail and slow the search down (an analogy is perhaps the Quick Sort which is known as the best sorting algorithm for the general case but no guarantee is given about its actual speed). If you prefer more conservative option, try BBPP or the even slower but more predictable IBB or BBB.