基于标签关联的多标签演化超网络

针对多标签学习中如何有效挖掘利用高阶标签关联的问题，提出了一种基于标签关联的多标签演化超网络模型.该模型通过输入任意多标签学习方法的预测结果，利用超边表征挖掘高阶标签关联，并综合标签关联和特征信息作为最终的预测结果.与3种传统多标签学习方法在6个多标签数据集上的对比实验表明，本文提出模型不仅能够有效提升多个传统多标签学习方法的性能，而且能够提供具有良好可读性的学习结果.

Multi-Label Evolutionary Hypernetwork Based on Label Correlations

In order to solve the problem that how to explore and exploit the high-order label correlations effectively in multi-label learning,a Multi-Label evolutionary HyperNetwork based on label Correlations (MLHNC) is proposed in this paper.In MLHNC,the predicting results obtained from any multi-label learning method are utilized as input of the model,the high-order correlations among labels are represented and explored by hyperedges,and the final prediction is made by integrating the label correlation and feature information.The experimental results on six multi-label datasets compared with three state-of-the-art multi-label learning methods show that the MLHNC not only improves the performance of various state-of-the-art multi-label learning methods,but also provides readable learning results.