Classifier chains for multi-label classification

Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757–1771.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

Cheng, W., & Hüllermeier, E. (2009). Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2–3), 211–225. doi: 10.1007/s10994-009-5127-5 .

Cheng, W., Dembczyński, K., & Hüllermeier, E. (2010). Bayes optimal multilabel classification via probabilistic classifier chains. In ICML ’10: 27th international conference on machine learning. Haifa: Omnipress.

Clare, A., & King, R. D. (2001). Lecture notes in computer science: Vol. 2168. Knowledge discovery in multi-label phenotype data.

Dembczyński, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010). On label dependence in multi-label classification. In Workshop proceedings of learning from multi-label data (pp. 5–12). Haifa, Israel.

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.

Dimou, A., Tsoumakas, G., Mezaris, V., Kompatsiaris, I., & Vlahavas, I. (2009). An empirical study of multi-label learning methods for video annotation. In Proceedings of the 7th international workshop on content-based multimedia indexing. New York: IEEE.

Elisseeff, A., & Weston, J. (2001). A kernel method for multi-labelled classification. In Advances in neural information processing systems (Vol. 14, pp. 681–687). Cambridge: MIT Press.

Fan, R. E., & Lin, C. J. (2007). A study on threshold selection for multi-label classification (Tech. rep.). National Taiwan University. http://www.csie.ntu.edu.tw/cjlin/papers/threshold.pdf .

Freund, Y., & Schapire, R. E. (1999). A short introduction to boosting. Jinkō Chinō Gakkaishi, 14(5), 771–780.

Fürnkranz, J. (2002). Round robin classification. Machine Learning, 2, 721–747.

Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine Learning, 73(2), 133–153. doi: 10.1007/s10994-008-5064-8 .

Godbole, S., & Sarawagi, S. (2004). Discriminative methods for multi-labeled classification. In PAKDD ’04: eighth Pacific-Asia conference on knowledge discovery and data mining (pp. 22–30). Berlin: Springer.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Peter, R., & Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explorations, 11(1).

Hsu, D., Kakade, S. M., Langford, J., & Zhang, T. (2009). Multi-label prediction via compressed sensing. In NIPS ’09: neural information processing systems 2009.

Ji, S., Tang, L., Yu, S., & Ye, J. (2008). Extracting shared subspace for multi-label classification. In KDD ’08: 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 381–389). New York: ACM. doi: 10.1145/1401890.1401939 .

Kiritchenko, S. (2005). Hierarchical text categorization and its application to bioinformatics. Ph.D. thesis, Queen’s University, Kingston, Canada.

Loza Mencía, E., & Fürnkranz, J. (2008). Efficient pairwise multilabel classification for large-scale problems in the legal domain. In ECML-PKDD ’08: European conference on machine learning and knowledge discovery in databases (pp. 50–65). Berlin: Springer. doi: 10.1007/978-3-540-87481-2_4 .

McCallum, A. K. (1999). Multi-label text classification with a mixture model trained by EM. In Association for the advancement of artificial intelligence workshop on text learning.

Petrovskiy, M. (2006). Paired comparisons method for solving multi-label learning problem. In HIS ’06: sixth international conference on hybrid intelligent systems. New York: IEEE. doi: 10.1109/HIS.2006.264925 .

Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers (pp. 61–74). Cambridge: MIT Press.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

Ráez, A. M., López, L. A. U., & Steinberger, R. (2004). Adaptive selection of base classifiers in one-against-all learning for large multi-labeled collections. In EsTAL: 4th international conference on advances in natural language processing (pp. 1–12).

Read, J., Pfahringer, B., & Holmes, G. (2008). Multi-label classification using ensembles of pruned sets. In ICDM’08: eighth IEEE international conference on data mining (pp. 995–1000). New York: IEEE.

Read, J., Pfahringer, B., & Holmes, G. (2009a). Generating synthetic multi-label data streams. In MLD ’09: 1st ECML/PKDD 2009 workshop on learning from multi-label data.

Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009b). Classifier chains for multi-label classification. In ECML ’09: 20th European conference on machine learning (pp. 254–269). Berlin: Springer.

Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3), 297–336.

Schapire, R. E., & Singer, Y. (2000). Boostexter: a boosting-based system for text categorization. Machine Learning, 39(2/3), 135–168.

Spyromitros, E., Tsoumakas, G., & Vlahavas, I. (2008). An empirical study of lazy multilabel classification algorithms. In SETN ’08: fifth Hellenic conference on artificial intelligence (pp. 401–406). Berlin: Springer.

Sun, L., Ji, S., & Ye, J. (2008). Hypergraph spectral learning for multi-label classification. In KDD ’08: 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 668–676). New York: ACM. doi: 10.1145/1401890.1401971 .

Tai, F., & Lin, H. T. (2010). Multi-label classification with principle label space transformation. In Workshop proceedings of learning from multi-label data, Haifa, Israel.

Tsoumakas, G., & Katakis, I. (2007). Multi label classification: an overview. International Journal of Data Warehousing and Mining, 3(3), 1–13.

Tsoumakas, G., & Vlahavas, I. P. (2007). Random k-labelsets: an ensemble method for multilabel classification. In ECML ’07: 18th European conference on machine learning (pp. 406–417). Berlin: Springer.

Vens, C., Struyf, J., Schietgat, L., Džeroski, S., & Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 2(73), 185–214. doi: 10.1007/s10994-008-5077-3 .

Yan, R., Tesic, J., & Smith, J. R. (2007). Model-shared subspace boosting for multi-label classification. In KDD ’07: 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 834–843). New York: ACM. doi: 10.1145/1281192.1281281 .

Yang, Y. (2001). A study on thresholding strategies for text categorization. In Proceedings of SIGIR-01, 24th ACM international conference on research and development in information retrieval (pp. 137–145). New York: ACM Press.

Zhang, M. L., & Zhou, Z. H. (2005). A k-nearest neighbor based algorithm for multi-label classification. In GnC ’05: IEEE international conference on granular computing (pp. 718–721). New York: IEEE.

Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038–2048.