梯度优化决策树的集成学习及其应用

Research and Application of Ensemble Learning Using Gradient Optimization Decision Tree

集成学习通过构建具有一定互补功能的多个分类器来完成学习任务,以减少分类误差。但是当前研究未能考虑分类器的局部有效性。为此,在基于集成学习的框架下,提出了一个分层结构的多分类算法。该算法按预测类别分解问题,在分层的基础上,集成多个分类器以提高分类准确度。在美国某高校招生录取这一个实际应用的数据集及3个UCI数据集上进行实验,实验结果验证了该算法的有效性。

Ensemble learning completes the learning task by building multiple classifiers with certain complementary performance to reduce the classification error.However,the current research fails to consider the local validity of the classifier.In this paper,a hierarchical multi-class classification algorithm was proposed in the framework of ensemble learning.The algorithm decomposes the problem by predicted category,and integrates several weak classifiers on the basis of stratification to improve the prediction accuracy.The experimental results on a real data set of American College Matriculation Set and three UCI datasets verified the effectiveness of the algorithm.