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:
- 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.
- G. Tsoumakas, I. Katakis, "Multi-Label Classification: An Overview", International Journal of Data Warehousing and Mining, 3(3):1-13, 2007.
- 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.
- 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.
- 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.
- 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.
- I. Katakis, G. Tsoumakas, I. Vlahavas, “Multilabel Text Classification for Automated Tag Suggestion”, Proceedings of the ECML/PKDD 2008 Discovery Challenge, Antwerp, Belgium, 2008.
- 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
- 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.
- 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.