一种用于方向预测的集成学习算法

对用方向向量标识示例的学习问题，以预测方向与实际方向之间的方向误差最小化为目标，提出了一种可用于方向预测的集成学习算法，详细分析了构造多个预测函数以及组合各个预测函数以实现方向的最优化预测方法. 提出的算法具有广泛的应用特性：当用不同的轴向来标识类别时，可简化得到多分类连续AdaBoost算法，其能确保训练错误率随分类器个数增加而降低；用错分代价组成的向量来标识示例时，可简化得到一种平均错分代价最小化的集成学习算法. 理论分析和实验结果均表明了算法的合理性和有效性.

An Ensemble Learning Algorithm for Direction Prediction

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.