, need to improve the methods based on LabelRank so that it can achieve its full potential

, we plan: (i) to consider more clever partition strategies to make the training more efficient; (ii) to rely on a non-uniform choice from the KNN classifier; (iii) to explore other classifiers; and (iv) to test other voting schemas, As future work

, Referências

D. Black, Partial justification of the Borda count, Public Choice, vol.28, issue.1, pp.1-15, 1976.
DOI : 10.1007/BF01718454

Z. Cheng, Z. Zhou, W. , and X. , Scientific Workflow Clustering and Recommendation, 2015 11th International Conference on Semantics, Knowledge and Grids (SKG), pp.272-274, 2015.
DOI : 10.1109/SKG.2015.52

URL : https://hal.archives-ouvertes.fr/hal-01461986

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, pp.21-27, 1967.
DOI : 10.1109/TIT.1967.1053964

J. Das, P. Mukherjee, S. Majumder, and P. Gupta, Clustering-based recommender system using principles of voting theory, 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp.230-235, 2014.
DOI : 10.1109/IC3I.2014.7019655

P. C. Fishburn, Simple voting systems and majority rule, Behavioral Science, vol.59, issue.3, pp.166-176, 1974.
DOI : 10.1002/bs.3830190303

J. Freire, D. Koop, E. Santos, and C. T. Silva, Provenance for Computational Tasks: A Survey, Computing in Science & Engineering, vol.10, issue.3, pp.20-30, 2008.
DOI : 10.1109/MCSE.2008.79

J. Fürnkranz and E. Hüllermeier, Preference learning, Encyclopedia of Machine Learning, pp.789-795, 2011.

A. Halioui, P. Valtchev, and A. B. Diallo, Towards an ontology-based recommender system for relevant bioinformatics workflows. bioRxiv, p.82776, 2016.
DOI : 10.1101/082776

URL : https://www.biorxiv.org/content/early/2016/10/24/082776.full.pdf

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.5-53, 2004.
DOI : 10.1145/963770.963772

URL : http://www.cs.orst.edu/~herlock/papers/tois2004.pdf

E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, Label ranking by learning pairwise preferences, Artificial Intelligence, vol.172, pp.16-171897, 2008.

R. Mukherjee, N. Sajja, and S. Sen, A movie recommendation system?an application of voting theory in user modeling, User Modeling and User-Adapted Interaction, vol.13, issue.1/2, pp.5-33, 2003.
DOI : 10.1023/A:1024022819690

K. A. Ocaña, D. De-oliveira, E. Ogasawara, A. M. Dávila, A. A. Lima et al., Sciphy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes, BSB11, pp.66-70, 2011.

J. Pessiot, T. Truong, N. Usunier, M. Amini, and P. Gallinari, Learning to rank for collaborative filtering, ICEIS 2007 -Proc. of the 9th International Conf. on Enterprise Information Systems, pp.145-151, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01336029

P. Refaeilzadeh, L. Tang, and H. Liu, Cross-validation. Encyclopedia of database systems, pp.1-7, 2016.

K. Soomro, K. Munir, and R. Mcclatchey, Incorporating semantics in pattern-based scientific workflow recommender systems: Improving the accuracy of recommendations, 2015 Science and Information Conference (SAI), pp.565-571, 2015.
DOI : 10.1109/SAI.2015.7237199

Y. Zhao, I. Raicu, and I. Foster, Scientific Workflow Systems for 21st Century, New Bottle or New Wine?, 2008 IEEE Congress on Services, Part I, pp.467-471, 2008.
DOI : 10.1109/SERVICES-1.2008.79

URL : http://people.cs.uchicago.edu/~iraicu/publications/2008_SWF08_eScience_short.pdf