Binary classification trees for multi-class classification problems

This paper proposes a binary classification tree aiming atsolving multi-class classification problems using binaryclassifiers. The tree design is achieved in a way that aclass group is partitioned into two distinct subgroups at anode. The node adopts the class-modular scheme toimprove the binary classification capability. Thepartitioning is formulated as an optimization problemand a genetic algorithm is proposed to solve theoptimization problem. The binary classification tree iscompared to the conventional methods in terms ofclassification accuracy and timing efficiency.Experiments were performed with numeral recognitionand touching-numeral pair recognition.