Separation of concerns in feature modeling, Proceedings of the 11th annual international conference on Aspect-oriented Software Development, AOSD '12, 2012. ,
DOI : 10.1145/2162049.2162051
URL : https://hal.archives-ouvertes.fr/hal-00767423
A Rule-Driven Approach for composing Viewpoint-oriented Models., The Journal of Object Technology, vol.9, issue.2, pp.89-114, 2010. ,
DOI : 10.5381/jot.2010.9.2.a1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.243
WISP: A pattern-based approach to the interchange of scientific workflow specifications, Concurrency and Computation: Practice and Experience, 2016. ,
DOI : 10.1109/services.2007.63
Software Product Lines : Practices and Patterns, 2001. ,
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems, Journal of Machine Learning Research, vol.15, pp.3133-3181, 2014. ,
A generic framework for analyzing model co-evolution, Proceedings of the Workshop on Models and Evolution co-located with MoDELS 2014, pp.12-21, 2014. ,
Consistency maintenance for evolving feature models, Expert Systems with Applications, vol.39, issue.5, pp.4987-4998, 2012. ,
DOI : 10.1016/j.eswa.2011.10.014
The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009. ,
DOI : 10.1145/1656274.1656278
ClowdFlows: A Cloud Based Scientific Workflow Platform, Machine Learning and Knowledge Discovery in Databases MLbase: A Distributed Machine-Learning System. In CIDR, pp.816-819, 2012. ,
DOI : 10.1007/978-3-642-33486-3_54
software product lines online tools, OOPSLA, pp.761-762, 2009. ,
Studying Evolving Software Ecosystems based on Ecological Models, Evolving Software Systems, pp.297-326, 2014. ,
DOI : 10.1007/978-3-642-45398-4_10
Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Software Product Line Engineering: Foundations, Principles and Techniques, 2005. ,
DOI : 10.1007/3-540-28901-1
Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio. https://azure.microsoft.com/en-us/documentation/ articles/machine-learning-algorithm-cheat-sheet ,
SPLEMMA, Proceedings of the 17th International Software Product Line Conference co-located workshops on, SPLC '13 Workshops, pp.59-66, 2013. ,
DOI : 10.1145/2499777.2500709
URL : https://hal.archives-ouvertes.fr/hal-00838642
On the modularization provided by concern-oriented reuse, Companion Proceedings of the 15th International Conference on Modularity, MODULARITY Companion 2016, pp.184-189, 2016. ,
DOI : 10.1145/2892664.2892697
Co-evolution of models and feature mapping in software product lines, Proceedings of the 16th International Software Product Line Conference on, SPLC '12 -volume 1, pp.76-85 ,
DOI : 10.1145/2362536.2362550
Handling complex configurations in software product lines, Proceedings of the 18th International Software Product Line Conference on, SPLC '14, pp.112-121, 2014. ,
DOI : 10.1145/2648511.2648523
URL : https://hal.archives-ouvertes.fr/hal-01023553
The Lack of A Priori Distinctions Between Learning Algorithms, Neural Computation, vol.5, issue.7, pp.1341-1390, 1996. ,
DOI : 10.1162/neco.1993.5.6.893