Abstract: To resolve the learning problem in which the instances are labeled by vectors, with the destination of direction error minimization between the direction represented by prediction vector and the direction represented by actual vector, an ensemble learning algorithm for direction prediction was proposed. The methods to construct multiple prediction functions and to combine them to realize the optimized prediction of instance directions were put forward. This algorithm is very general. When the different classes are labeled by the different direction vectors of axes, the proposed algorithm is degenerated to real AdaBoost algorithm for multiclass classification, guaranteeing that the training error of the combination classifier can
be reduced while the number of trained classifiers increases. When the instances are labeled by the vector composed of the classification costs of all classes, the proposed algorithm is degenerated to an ensemble learning algorithm for costsensitive classification which can minimize average classification cost. The theoretical analysis and experimental results show that the proposed algorithm is reasonable and effective.

Key words: structured prediction, direction prediction, fuzzy classification, costsensitive, AdaBoost algorithm