基于双标签集的标签匹配集成学习算法

当标识示例的两个标签分别来源于两个标签集时，这种多标签分类问题称之为标签匹配问题，目前还没有针对标签匹配问题的学习算法。 尽管可以用传统的多标签分类学习算法来解决标签匹配问题，但显然标签匹配问题有其自身特殊性。 通过对标签匹配问题进行深入的研究，在连续AdaBoost(real Adaptive Boosting)算法的基础上，基于整体优化的思想，采用算法适应的方法，提出了基于双标签集的标签匹配集成学习算法，该算法能够较好地学习到标签匹配规律从而完成标签匹配。 实验结果表明，与传统的多标签学习算法用于解决标签匹配问题相比，提出的新算法不仅缩小了搜索的标签空间的范围，而且最小化学习误差可以随着分类器个数的增加而降低，进而使得标签匹配分类更加快速、准确。

Ensemble learning algorithm for labels matching based on pairwise labelsets

It is called labels matching problem when two labels of an instance come from two labelsets respectively in multi-label classification, however there is no any specific algorithm for solving such problem. Although the labels matching problem could be solved by tranditional multi-label classification algorithms, but this problem has its own particularity. After analyzing the labels matching problem, a new labels matching algorithm based on pairwise labelsets was proposed using adaptive method, which considered the real Adaptive Boosting (real AdaBoost) and the global optimization idea. This algorithm could learn the rule of labels matching well and complete matching. The experimental results show that, compared with the traditional algorithms, the new algorithm can not only reduce searching scope of the labels space, but also decrease the minimum learning error as the number of weak classifiers increases, and make the classification more accurate and faster.