Multi-class ADTboost
Martin Drauschke · 2008
This technical report gives a short review on boosting with alter-nating decision trees (ADTboost), which has been proposed by Freund & Mason (1999) and refined by De Comite ́ et al. (2001). This approach is designed for two-class problems, and we extend it towards multi-class classification. The advantage of a multi-class boosting algorithm is its usage in scene interpretation with various kinds of objects. In these cases, two-class approaches will lead to several one class versus background (the other classes) classifications, where we must solve unappropriate results like ”always background ” or ”two or more valid classes ” for a sample. 1