References
- Boutell, M.R., Luo, J., Shen, X. and Brown, C.M. (2004). Learning multi-label scene classification, Pattern Recognition 37(9): 1757-1771.
- Clare, A. and King, R.D. (2001). Knowledge discovery in multi-label phenotype data, in L.D. Raedt and A. Siebes (Eds.), PKDD: 5th European Conference on Machine Learning and Knowledge Discovery, Lecture Notes in Computer Science, Vol. 2168, Springer, Berlin/Heidelberg, pp. 42-53.
- Crammer, K. and Singer, Y. (2003). A family of additive online algorithms for category ranking, Journal of Machine Learning Research 3: 1025-1058.
- Dietterich, T.G. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research 2: 263-286.
- Diplaris, S., Tsoumakas, G., Mitkas, P. and Vlahavas, I. (2005). Protein classification with multiple algorithms, in P. Bozanis and E.N. Houstis (Eds.), 10th Panhel-llenic Conference on Informatics (PCI 2005), Lecture Notes in Computer Science, Vol. 3746, Springer-Verlag, Berlin/Heidelberg, pp. 448-456.
- Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K. and Poo, A.N. (2003). Multi-Category Classification by Soft-Max Combination of Binary Classifiers, Lecture Notes in Computer Science, Vol. 2709, Springer, Berlin/Heidelberg.
- Elisseeff, A. and Weston, J. (2001). A kernel method for multi-labelled classification, in T.G. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14, MIT Press, Cambridge, MA, pp. 681-687.
- Ferng, C.-S. and Lin, H.-T. (2011). Multi-label classification with error-correcting codes, Journal of Machine Learning Research 20: 281-295.
- Ghamrawi, N. and McCallum, A. (2005). Collective multi-label classification, in O. Herzog, H.-J. Schek, N. Fuhr, A. Chowdhury and W. Teiken (Eds.), International Conference on Information and Knowledge Management, CIKM, ACM, New York, NY, pp. 195-200.
- Hong, J., Min, J., Cho, U. and Cho, S. (2008). Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers, Pattern Recognition 41(2): 662-671.
- Hullermeier, E., Furnkranz, J., Cheng, W. and Brinker, K. (2008). Label ranking by learning pairwise preferences, Artificial Intelligence 172(16-17): 1897-1916.
- Jankowski, N. (2012). Graph-based generation of a meta-learning search space. International Journal of Applied Mathematics and Computer Science 22(3): 647-667, DOI: 10.2478/v10006-012-0049-y
- Kajdanowicz, T. and Kazienko, P. (2009a). Hybrid repayment prediction for debt portfolio, in N.T. Nguyen, R. Kowalczyk and S.-M. Chen (Eds.), Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, Lecture Notes in Artificial Intelligence, Vol. 5796, Springer, Berlin/Heidelberg, pp. 850-857.
- Kajdanowicz, T. and Kazienko, P. (2009b). Prediction of sequential values for debt recovery, in E. Bayro-Corrochano and J.-O. Eklundh (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, Vol. 5856, Springer, Berlin/Heidelberg, pp. 337-344.
- Kajdanowicz, T., Wozniak, M. and Kazienko, P. (2011). Multiple classifier method for structured output prediction based on error correcting output codes, in N. Nguyen, C.-G. Kim and A. Janiak (Eds.), Intelligent Information and Database Systems, Lecture Notes in Computer Science, Vol. 6592, Springer, Berlin/Heidelberg, pp. 333-342.
- Kuncheva, L.I. (2005). Using diversity measures for generating error-correcting output codes in classifier ensembles, Pattern Recognition Letters 26(1): 83-90.
- Kuriata, E. (2008). Creation of unequal error protection codes for two groups of symbols, International Journal of Applied Mathematics and Computer Science 18(2): 251-257, DOI: 10.2478/v10006-008-0023-x.
- Loza Mencia, E. and Furnkranz, J. (2008). Pairwise learning of multilabel classifications with perceptrons, Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN-08), Hong Kong, China, pp. 2900-2907.
- Mackay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press, Cambridge.
- Morelos-Zaragoza, R. (2006). The Art of Error Correcting Coding, Wiley, West Sussex.
- Pestian, J., Brew, C, Matykiewicz, P., Hovermale, D., Johnson, N., Bretonnel Cohen, K. and Duch, W. (2007). A shared task involving multi-label classification of clinical free text, Proceedings of ACL BioNLP, Association of Computational Linguistics, Stroudsburg, PA.
- Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2009). Classifier chains for multi-label classification, 13th European Conference on Principles and Practice of Knowledge Discovery in Databases/20th European Conference on Machine Learning, Bled, Slovenia, pp. 254-269.
- Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2011). Classifier chains for multi-label classification, Machine Learning 85(3): 333-359.
- Reed, I.S. and Chen, X. (1999). Error-Control Coding for Data Networks, Kluwer Academic Publishers, Norwell, MA.
- Sammut, C. and Webb, G.I. (2011). Encyclopedia of Machine Learning, Springer, Berlin/Heidelberg.
- Schapire, R.E. and Singer, Y. (2000). Boostexter: A boosting-based system for text categorization, Machine Learning 39(2/3): 135-168.
- Trohidis, K., Tsoumakas, G., Kalliris, G. and Vlahavas, I. (2008). Multilabel classification of music into emotions, 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA, pp. 325-330.
- Tsoumakas, G., Katakis, I. and Vlahavas, I. (2011). Random k-labelsets for multilabel classification, IEEE Transactions on Knowledge and Data Engineering 23(7): 1079-1089.
- Tsoumakas, G. and Vlahavas, I. (2007). Random k-labelsets: An Ensemble Method for Multilabel Classification, Lecture Notes in Artificial Intelligence, Vol. 4701, Springer, Berlin/Heidelberg.
- Zhang, M.-L. and Zhou, Z.-H. (2006). Multilabel neural networks with applications to functional genomics and text categorization, IEEE Transactions on Knowledge and Data Engineering 18(10): 1338-1351.
- Zhang, M. and Zhou, Z. (2007). ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition 40(7): 2038-2048.
- Zhang, Y. and Schneider, J. (2011). Multi-label output codes using canonical correlation analysis, Journal of Machine Learning Research 15: 873-882.