Multi-label classification using error correcting output codes
Open Access
|Dec 2012References
- Boutell, M.R., Luo, J., Shen, X. and Brown, C.M. (2004). Learning multi-label scene classification,(9): 1757-1771.
- Clare, A. and King, R.D. (2001). Knowledge discovery in multi-label phenotype data,L.D. Raedt and A. Siebes (Eds.),, 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,: 1025-1058.
- Dietterich, T.G. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes,: 263-286.
- Diplaris, S., Tsoumakas, G., Mitkas, P. and Vlahavas, I. (2005). Protein classification with multiple algorithms,P. Bozanis and E.N. Houstis (Eds.),, 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)., Lecture Notes in Computer Science, Vol. 2709, Springer, Berlin/Heidelberg.
- Elisseeff, A. and Weston, J. (2001). A kernel method for multi-labelled classification,T.G. Dietterich, S. Becker and Z. Ghahramani (Eds.),, MIT Press, Cambridge, MA, pp. 681-687.
- Ferng, C.-S. and Lin, H.-T. (2011). Multi-label classification with error-correcting codes,: 281-295.
- Ghamrawi, N. and McCallum, A. (2005). Collective multi-label classification,O. Herzog, H.-J. Schek, N. Fuhr, A. Chowdhury and W. Teiken (Eds.),, 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,(2): 662-671.
- Hullermeier, E., Furnkranz, J., Cheng, W. and Brinker, K. (2008). Label ranking by learning pairwise preferences,(16-17): 1897-1916.
- Jankowski, N. (2012). Graph-based generation of a meta-learning search space.(3): 647-667, DOI: 10.2478/v10006-012-0049-y
- Kajdanowicz, T. and Kazienko, P. (2009a). Hybrid repayment prediction for debt portfolio,N.T. Nguyen, R. Kowalczyk and S.-M. Chen (Eds.),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,E. Bayro-Corrochano and J.-O. Eklundh (Eds.),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,N. Nguyen, C.-G. Kim and A. Janiak (Eds.),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,(1): 83-90.
- Kuriata, E. (2008). Creation of unequal error protection codes for two groups of symbols,(2): 251-257, DOI: 10.2478/v10006-008-0023-x.
- Loza Mencia, E. and Furnkranz, J. (2008). Pairwise learning of multilabel classifications with perceptrons,pp. 2900-2907.
- Mackay, D.J.C. (2003).Cambridge University Press, Cambridge.
- Morelos-Zaragoza, R. (2006).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,Association of Computational Linguistics, Stroudsburg, PA.
- Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2009). Classifier chains for multi-label classification,pp. 254-269.
- Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2011). Classifier chains for multi-label classification,(3): 333-359.
- Reed, I.S. and Chen, X. (1999)., Kluwer Academic Publishers, Norwell, MA.
- Sammut, C. and Webb, G.I. (2011)., Springer, Berlin/Heidelberg.
- Schapire, R.E. and Singer, Y. (2000). Boostexter: A boosting-based system for text categorization,(2/3): 135-168.
- Trohidis, K., Tsoumakas, G., Kalliris, G. and Vlahavas, I. (2008). Multilabel classification of music into emotions,, pp. 325-330.
- Tsoumakas, G., Katakis, I. and Vlahavas, I. (2011). Random k-labelsets for multilabel classification,(7): 1079-1089.
- Tsoumakas, G. and Vlahavas, I. (2007)., 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,(10): 1338-1351.
- Zhang, M. and Zhou, Z. (2007). ML-KNN: A lazy learning approach to multi-label learning,(7): 2038-2048.
- Zhang, Y. and Schneider, J. (2011). Multi-label output codes using canonical correlation analysis,: 873-882.
Language: English
Page range: 829 - 840
Published on: Dec 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2012 Tomasz Kajdanowicz, Przemysław Kazienko, published by University of Zielona Góra
This work is licensed under the Creative Commons License.