S. Asaduzzaman et al., “Anticipation of the significance of risk factors in cervical cancer for low incoming country: Bangladesh perspective,” International Journal of Scientific & Engineering Research, vol.6, no.11, pp. 876-881, 2015.
K. Thangavel, P.P. Jaganathan, and P.O. Easmi, “Data mining approach to cervical cancer patients analysis using clustering technique,” Asian Journal of Information Technology, vol.5, no.4, pp. 413-417, 2006.
World Health Organization. 2008-2013 action plan for the global strategy for the prevention and control of non-communicable disease. Geneva; 2008, [Online]. Available: http://apps.who.int/iris/bitstream/10665/44009/1/9789241597418_eng.pdf
J. Read et al., “Meka: a multi-label/multi-target extension to weka,” Journal of Machine Learning Research, vol. 17, pp.1-5, 2016.
M. Boutell et al., “Multi-label semantic scene classification”, Department of Computer Science University of Rochester, USA, Tech. Rep. Sept. 2003.
A. K. McCallum, “Multi-label text classification with a mixture model trained by EM,” in Proc. of the AAAI 99 Workshop on Text Learning, 1999, pp. 1–7.
N. Ueda and K. Saito, “Parametric mixture models for multi-label text”, in: Advances in Neural Information Processing Systems, vol. 15, S. Becker, S. Thrun, K. Obermayer, Ed. MIT Press, Cambridge, MA, 2003, pp. 721–728.
C. Vens et al., “Decision trees for hierarchical multi-label classification,” Machine Learning, vol. 73, no. 2, pp. 185- 214, 2008.
T. Li, C. Zhang, and S. Zhu, “Empirical studies on multi-label classification,” in Proc. of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006, pp. 86-92.
K. Trohidis et al., “Multi-label classification of music into emotions,” in Proc. of the 9th International Conference on Music Information Retrieval, 2008, pp. 325-330.
G. Tsoumakas, and I. Katakis, “Multi label classification: an overview,” International Journal of Data Warehousing and Mining, vol.3, no. 3, pp. 1–13, 2007.
M.L. Zhang and Z.H. Zhou, “ML-kNN: a lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no.7, pp. 2038–2048, 2007.
A. Elisseeff and J. Weston, “A Kernel method for multi-labelled classification,” in: Proc. of the Annual ACM Conference on Research and Development in Information Retrieval, 2005, pp. 274–281.
M.L. Zhang and Z.H. Zhou, “Multi-label neural networks with applications to functional genomics and text categorization,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no.10, pp. 1338–1351, 2006.
A. Clare and R.D. King, “Knowledge discovery in multi-label phenotype data, ” in: Proc. of the 5th European Conference on PKDD, 2001, pp. 42–53.
A. Clare and R. D. King, “Knowledge Discovery in Multi-Label Pheno- type Data,” in: Lecture Notes in Computer Science. Springer, 2001.
J. Read et al., “Classifier Chains for Multi-label Classification,” in: Proc. of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II. ECML PKDD ’09. Bled, Slovenia: Springer-Verlag, 2009, pp. 254–269.
G. Tsoumakas and I. Vlahavas, “Random k-labelsets: an ensemble method for multi-label classification, “ in: Proceedings of the 18th European conference on Machine Learning, 2007, pp. 406–417.
Y. Guo and S. Gu, “Multi-label classification using conditional dependency networks,” in: Proc. of the Twenty-Second international joint conference on Artificial Intelligence, Barcelona, Catalonia, Spain, 2011, pp. 16-22.
J. Read, L. Martino, and J. Hollmén, “Multi-label methods for prediction with sequential data,” Pattern Recognition, vol. 63, pp. 45-55, 2017.
J. Read et al., “Scalable multi-output label prediction: From classifier chains to classifier trellises,” Pattern Recognition, vol. 48, no.6, pp. 2096-2109, 2015.
UCI machine learning repository, 2017, [Online]. Available:
G. Madjarov et al., “An extensive experimental comparison of methods for multi-label learning,” Pattern Recognition, vol. 45, no. 9, pp. 3084-3104, 2012.
H. Modi and M. Panchal, “Experimental comparison of different problem transformation methods for multi-label classification using MEKA,” International Journal of Computer Applications, vol 59, No. 15, pp. 10-15, 2012.
F. Herrera et al., “Multi-label Classification: Problem Analysis, Metrics and Techniques,” Springer, 2016.
W. Gao, and Z.-H. Zhou, “On the consistency of multi-label learning,” Artificial Intelligence, vol. 199, pp. 22-44, 2013.