传统分类问题，即多类分类问题是，假设每个示例仅具有单个标记，且所有样本的标签类别数|L|大于1，然而，在很多现实世界的应用中，往往存在单个示例同时具有多重标记的情况。 而在多分类问题中，每个样本所含标签是类别集合的非空子集，近年来，在机器学习和数据挖掘等相关领域，多类分类问题得到广泛研究。其原因主要有：1. 应用领域非常广泛。如，多媒体信息检索，推荐，查询分类，医疗诊断等。2. 一些挑战性的研究问题涉及到多类分类问题。例如，处理能从大量类别中，处理稀少类别并且发现之间的关系等。

 目前，对多标记分类问题方法研究主要集中在以下两个方面：首先是问题转换方法，即改造数据使其适应现有算法的方法，该类方法主要通过对多标记训练数据样本进行处理，将多标记学习问题转换为其它已知的学习问题进行求解；其次是算法适应方法，即改造现有算法使其适应数据样本，该类方法是通过对传统的机器学习方法进行扩展或改进，使其适应多标记数据学习问题。

已有不少处理多标记学习问题的框架，例如mulan还是非常方便的，Mulan中提供了很多相关算法，对weka熟悉的话拿来稍微熟悉下就可以了。它和weka一样的开源，在mulan.examples下有示例函数。

下载安装详细流程：http://mulan.sourceforge.net/download.html

这里列出关于多标记学习的一些相关文献：

1. G. Tsoumakas, I. Katakis, I. Vlahavas, "A Review of Multi-Label Classification Methods", in: Proceedings of the 2nd ADBIS Workshop on Data Mining and Knowledge Discovery (ADMKD 2006), pp 99-109, September 2006, Thessaloniki, Greece.
2. G. Tsoumakas, I. Katakis, "Multi-Label Classification: An Overview", International Journal of Data Warehousing and Mining, 3(3):1-13, 2007.
3. G. Tsoumakas, I. Vlahavas, "Random k-Labelsets: An Ensemble Method for Multilabel Classification", Proc. 18th European Conference on Machine Learning (ECML 2007), pp. 406-417, Warsaw, Poland, 17-21 September 2007.
4. K. Trohidis, G. Tsoumakas, G. Kalliris, I. Vlahavas. "Multilabel Classification of Music into Emotions". Proc. 9th International Conference on Music Information Retrieval (ISMIR 2008), pp. 325-330, Philadelphia, PA, USA, 2008.
5. E. Spyromitros, G. Tsoumakas, I. Vlahavas, “An Empirical Study of Lazy Multilabel Classification Algorithms”, Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008), Springer, Syros, Greece, 2008.
6. G. Tsoumakas, I. Katakis, I. Vlahavas, “Effective and Efficient Multilabel Classification in Domains with Large Number of Labels”, Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD‘08), Antwerp, Belgium, 2008.
7. I. Katakis, G. Tsoumakas, I. Vlahavas, “Multilabel Text Classification for Automated Tag Suggestion”, Proceedings of the ECML/PKDD 2008 Discovery Challenge, Antwerp, Belgium, 2008.
8. A. Dimou, G. Tsoumakas, V. Mezaris, I. Kompatsiaris, I. Vlahavas, “An Empirical Study Of Multi-Label Learning Methods For Video Annotation”, 7th International Workshop on Content-Based Multimedia Indexing, IEEE, Chania, Crete, 2009
   • [cbmi09-bow.rar] [cbmi09-mpeg.rar]
9. G. Nasierding, G. Tsoumakas, A. Kouzani, “Clustering Based Multi-Label Classification for Image Annotation and Retrieval”, 2009 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2009.
10. G. Tsoumakas, A. Dimou, E. Spyromitros, V. Mezaris, I. Kompatsiaris, I. Vlahavas, “Correlation-Based Pruning of Stacked Binary Relevance Models for Multi-Label Learning”, Proceedings of the 1st International Workshop on Learning from Multi-Label Data (MLD‘09), G. Tsoumakas, Min-Ling Zhang, Zhi-Hua Zhou (Ed.), pp. 101-116, Bled, Slovenia, 2009.

多标记学习--Learning from Multi-Label Data