计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 121-125.
王延斌, 武优西, 刘洪普
WANG Yan-bin, WU You-xi, LIU Hong-pu
摘要： 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。
中图分类号:
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