The week's pick
Random Articles
Reseach Article
Study and Analysis of Multi-Label Classification Methods in Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 159 - Number 9 |
| Year of Publication: 2017 |
| Authors: Shubhangi R. Khade, Suraj R. Balwan |
10.5120/ijca2017913035
|
Shubhangi R. Khade, Suraj R. Balwan . Study and Analysis of Multi-Label Classification Methods in Data Mining. International Journal of Computer Applications. 159, 9 ( Feb 2017), 9-12. DOI=10.5120/ijca2017913035
Abstract
Multi-label classification is major research problem in machine learning domain. Multi-label classification is nothing but the variants of classification problem in which different target labels should be allocated to every instance. Multi-label classification is different from the multiclass classification. In general, multi-label classification is defined as problem of searching model which maps the input to binary vectors, rather than outputs in scalars. Basically there are two different techniques for handling the multi-label classification problem such as techniques of problem transformation and techniques of algorithm adaptation. In problem transformation approaches, multi-label classification problem is transformed to binary classification problems set and this can be further processed through single class classifiers. In algorithm adaptation approaches, algorithms are adapted in order to perform the multi-label classification directly. In this paper, different multi-label classification algorithms are studied and evaluated with current research problems. Methods such as binary relevance (BR), high-order approaches, hierarchical tree based algorithms, and the most recent method called ML-Forest are studied and evaluated with different real time datasets such as medical, emotions, yeast etc.
References
- Qingyao Wu, Mingkui Tan, Hengjie Song, “ML-FOREST: A Multi-label Tree Ensemble Method for Multi-Label Classification”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, MAY 2016.
- T. N. Rubin, A. Chambers, P. Smyth, and M. Steyvers, “Statistical topic models for multi-label document classification,” Machine learning, vol. 88, no. 1-2, pp. 157–208, 2012.
- Z.-H. Zhou, M.-L. Zhang, S.-J. Huang and Y.-F. Li, “Multi-instance multi-label learning,” Artificial Intelligence, vol. 176, no. 1, pp. 2291–2320, 2012.
- M. Liu, Y. Luo, D. Tao, C. Xu, and Y. Wen, “Low-rank multi-view learning in matrix completion for multi-label image classification,” in Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
- F. Sun, J. Tang, H. Li, G.-J. Qi, and T. S. Huang, “Multi-label image categorization with sparse factor representation,” Image Processing, IEEE Transactions on, vol. 23, no. 3, pp. 1028–1037, 2014.
- X. Xiao, P. Wang, W.-Z. Lin, J.-H. Jia, and K.-C. Chou, “iamp-2l: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types,” Analytical biochemistry, vol. 436, no. 2, pp. 168–177, 2013.
- K.-C. Chou, “Some remarks on predicting multi-label attributes in molecular biosystems,” Molecular Biosystems, vol. 9, no. 6, pp. 1092–1100, 2013.
- H. Blockeel, L. De Raedt, and J. Ramon, “Top-down induction of clustering trees,” arXiv preprint cs/0011032, 2000.
- G. Tsoumakas and I. Katakis, “Multi-label classification: An overview,” International Journal of Data Warehousing & Mining, vol. 3, no. 3, pp. 1–13, 2007.
- G. Tsoumakas, I. Katakis, and I. Vlahavas, “Effective and efficient multilabel classification in domains with large number of labels,” in Proc. ECML/PKDD’08 Workshop on Mining Multidimensional Data, 2008, pp. 30–44.
- W. Cheng, E. H¨ ullermeier, and K. J. Dembczynski, “Bayes optimal multilabel classification via probabilistic classifier chains,” in Proceedings of ICML’10 the 27th International Conference on Machine Learning, 2010, pp. 279–286.
- J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” Machine learning, vol. 85, no. 3, pp. 333–359, 2011.
- G. Madjarov, D. Gjorgjevikj, and S. Dˇzeroski, “Two stage architecture for multi-label learning,” Pattern Recognition, vol. 45, no. 3, pp. 1019–1034, 2012.
Index Terms
Keywords