REFERENCES
- Abeel, T., T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys. 2010. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3):392–398.
- Bartko, J. 1976. On various intraclass correlation reliability coefficients. Psychological Bulletin 83(5):762.
- Budnik, M., and B. Krawczyk. 2013. On optimal settings of classification tree ensembles for medical decision support. Health Informatics Journal 19(1):3–15.
- Conitzer, V. 2006. Computational aspects of preference aggregation. PhD thesis, IBM.
- Borda, J. C. de. 1781. Memoire sur les Elections au Scrutin. Histoire de l‘Academie Royale des Sciences, Paris.
- Dwork, C., R. Kumar, M. Naor, and D. Sivakumar. 2001. Rank aggregation methods for the web. In Proceedings of the 10th international conference on world wide web, 613–622. ACM.
- Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. The Journal of Machine Learning Research 3: 1157–1182.
- Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The weka data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1):10–18.
- Hettich, S., and S. Bay. 1999. The uci kdd archive.
- Kemeny, J. 1959. Mathematics without numbers. Daedalus 88(4):577–591.
- Khoussainov, R., A. Hess, and N. Kushmerick. 2005. Ensembles of biased classifiers. In Proceedings of the 22nd international conference on machine learning, 425–432. ACM
- Pathical, S. P. 2010. Classification in high dimensional feature spaces through random subspace ensembles. PhD thesis, University of Toledo.
- Piateski, G., and W. Frawley. 1991. Knowledge discovery in databases. Cambridge, MA, USA: MIT press.
- Pomeroy, S. L., P. Tamayo, M. Gaasenbeek, L. M. Sturla, M. Angelo, M. E. McLaughlin, J. Y. Kim, L. C. Goumnerova, P. M. Black, C. Lau, et al. 2002. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870):436–442.
- Saeys, Y., I. Inza, and P. Larrañaga. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517.
- Sarkar, C., S. Cooley, and J. Srivastava. 2012. Improved feature selection for hematopoietic cell transplantation outcome prediction using rank aggregation. In Federated Conference on Computer Science and Information Systems (FedCSIS), 2012, 221–226. IEEE.
- Subbian, K., and P. Melville. 2011. Supervised rank aggregation for predicting influence in networks. arXiv preprint arXiv:1108.4801.
- Termenon, M., M. Graña, A. Besga, J. Echeveste, and A. Gonzalez-Pinto. 2012. Lattice independent component analysis feature selection on diffusion weighted imaging for Alzheimer’s disease classification. Neurocomputing 114:132–141.
- Vilalta, R., and D. Oblinger. 2000. A quantification of distance bias between evaluation metrics in classification. In Proceedings of the international conference on machine learning, 1087–1094. Stanford University, Stanford, CA, June.
- Wang, G., and Q. Song. 2012. Selecting feature subset for high dimensional data via the propositional foil rules. Pattern Recognition 45(7):2672–2689.
- Woźniak, M., M. Graña, and E. Corchado. 2014. A survey of multiple classifier systems as hybrid systems. Information Fusion 16:3–17.
- Yang, Y., and J. Pedersen. 1997. A comparative study on feature selection in text categorization. In Machine learning-international workshop then conference, 412–420. Morgan Kaufmann.
- Yu, L., and H. Liu. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Machine learning-international workshop then conference 20:856.