Learning from Multi-Label Data

Introduction

Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label l from a set of disjoint labels L, |L| > 1. In multi-label classification, the examples are associated with a set of labels Y in L. In the past, multi-label classification was mainly motivated by the tasks of text categorization and medical diagnosis. Nowadays, we notice that multilabel classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification.

Mulan: An Open Source Java Library for Multi-Label Learning

Mulan has now its own web site!

We have developed and are constantly enriching an open source Java library for Multi-label learning, called Mulan. Mulan contains several problem transformation and algorithm adaptation methods for multilabel classification and ranking, an evaluation framework that computes several multilabel classification evaluation measures and a class providing data set statistics. It also contains an algorithm and support for hierarchical multi-label classification. Mulan is built on top of Weka and it therefore utilizes its award-wining code base. It is open-source and distributed under the GNU GPL licence. Please contact Grigorios Tsoumakas for bug reports, comments, suggestions or request for help with the library.

Publications

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.
11. G. Tsoumakas, E. Loza Mencia, I. Katakis, S. Park, J. Furnkrnaz, “On the combination of two decompositive multi-label classification methods”, Workshop on Preference Learning, ECML PKDD 09, Eyke Hullermeir, Johannes Furnkranz (Ed.), pp. 114-133, Bled, Slovenia, 2009.
12. G. Tsoumakas, I. Katakis, I. Vlahavas, "Mining Multi-label Data", Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, 2010.
13. G. Nasierding, A. Kouzani, G. Tsoumakas, “A Triple-Random Ensemble Classification Method for Mining Multi-label Data”, Proc. 2010 IEEE International Conference on Data Mining Workshops, pp. 49-56, 2010.
14. M. Ioannou, G. Sakkas, G. Tsoumakas, I. Vlahavas, “Obtaining Bipartitions from Score Vectors for Multi-Label Classification”, 22nd International Conference on Tools with Artificial Intelligence, 27-29 October 2010., IEEE, Arras, France, 2010.
15. E. Spyromitros-Xioufis, G. Tsoumakas, I. Vlahavas, “Multi-label Learning Approaches for Music Instrument Recognition”, Proc. 9th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 2011
16. G. Tsoumakas, I. Katakis, I. Vlahavas, “Random k-Labelsets for Multi-Label Classification”, IEEE Transactions on Knowledge and Data Engineering, IEEE, 23(7), pp. 1079-1089, 2011.
17. E. Spyromitros-Xioufis, M. Spiliopoulou, G. Tsoumakas, I. Vlahavas, "Dealing with Concept Drift and Class Imbalance in Multi-label Stream Classification", Proc. 22nd International Conference on Artificial Intelligence (IJCAI 2011), AAAI press, Barcelona, Spain, 2011.
18. G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, I. Vlahavas, “Mulan: A Java Library for Multi-Label Learning”,Journal of Machine Learning Research, 12, pp. 2411-2414, 2011
19. K. Sechidis, G. Tsoumakas, I. Vlahavas, “On the Stratification of Multi-Label Data”, Proceedings of ECML PKDD 2011, Athens, Greece, 2011.

Bibliography

Have a look at our new online multi-label learning bibliography at CiteULike (100 papers, September, 2009). Much more useful, as you can grab BibTeX and RIS records, subscribe to the corresponding RSS feed, follow links to the papers' full pdf (may require access to digital libraries) and export the complete bibliography for BibTeX or EndNote use (requires CiteULike account).