A branch-and-bound algorithm for optimization of multiperspective classifier

The present paper is devoted to the multiperspective recognition, in which the pattern to be recognized undergoes several classification tasks. Each task denotes here recognition from a different point of view and with respect to a different set of classes. In the decomposed dependent approach, when the multiperspective recognition is not a single activity but it states the multistep decision process, important role plays the order of recognition tasks determining the successive steps of the entire multiperspective recognition. In this paper the algorithm for optimal (with respect to the risk function) ordering of recognition tasks is presented. The proposed algorithm using controlled enumeration through a branch-and-bound search procedure selects the best order without exhaustive search. Furthermore, results of computer experiments are given and a simple illustrative example is considered.