Binary classification tree for multiclass classification with observation-based clustering

Many classification techniques are originally designed to solve a binary problem, but practically many classification problems involve more than two classes. A multiclass problem can be decomposed into binary sub-problems, each solved by a binary classifier. Aside from using one-against-one (OAO) or one-against-all (OAA) decomposition scheme, an ensemble of binary classifiers be constructed hierarchically. In this study, we focus in multiclass classification with a binary classification tree and propose a new approach in splitting a top-down tree by grouping observations into two clusters. Unlike a traditional class-clustering approach, this observation-based algorithm allows one class to appear in two meta-classes. The experiment shows how our proposed BCT-OB performed, compared with other binary classification tree algorithms. Then advantages and disadvantages of the algorithm are discussed.