Comparison of Multi-Label Classification Methods for Prediagnosis of Cervical Cancer
DOI:
https://doi.org/10.18201/ijisae.2017533896Keywords:
Multi-label classification, cervical cancer, risk factorsAbstract
Cervical cancer is one of the most common causes of cancer death of women. Prediagnosis of cervical cancer at early stages is critical to reduce mortality ratios. Additionally, early prediction of cervical cancer can help both the patients and the physicians depending on easiness of treatment. Cervical cancer results from various risk factors such as family history, education level, having multiple full-term pregnancies, smoking, and sexually transmitted diseases and etc. Recently, different types of advanced methods were developed for risk prediction analysis based on machine learning techniques. The purpose of this study is to investigate the efficacy of using multi-label classification techniques for diagnosing cervical cancer at early stage. Four common learning algorithms such as Naïve Bayes, J48 Decision Tree, Sequential Minimal Optimization, and Random Forest were compared in terms of their accuracy, hamming loss, exact match (subset accuracy) and ranking loss performance evaluation metrics. Thus, this study can help to physicians, academics and cancer researchers to make fast and accurate diagnosis.
Downloads
References
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:
https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29
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.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
