基于标记分布学习的异态集成学习算法

Heterogeneous Ensemble Learning Algorithm Based on Label Distribution Learning

为了提高预测的准确性,文中结合机器学习中堆积(Stacking)集成框架,组合多个分类器对标记分布进行学习,提出基于标记分布学习的异态集成学习算法(HELA-LDL).算法构造两层模型框架,通过第一层结构将样本数据采用组合方式进行异态集成学习,融合各分类器的学习结果,将融合结果输入到第二层分类器,预测结果是带有置信度的标记分布.在专用数据集上的对比实验表明,HELA-LDL可以发挥各种算法在不同场景下的性能较优,稳定性分析进一步说明算法的有效性.

To improve prediction accuracy,a stacking integration framework in machine learning is employed to learn label distribution through multiple classifiers,and a heterogeneous ensemble learning algorithm based on label distribution learning(HELA-LDL)is proposed.A two-layer model framework is constructed,and the sample data are combined through the first layer structure to integrate the learning results of each classifier.Finally,the fusion results are input to the second layer classifier as the original feature,and the labels are predicted to be a label distribution with confidence.Comparative experiments on specialized datasets show that HELA-LDL is superior to other algorithms in various scenes.The stability analysis further illustrates the effectiveness of HELA-LDL.